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Goal-oriented dead reckoning for autonomous characters
Computers & Graphics 25 (2001) 999–1011
Goal-oriented dead reckoning for autonomous characters ! M. Costa D. Szwarcman, B. Feijo*, # de Sao ICAD-Intelligent CAD Laboratory, Department of Informatics, PUC-Rio, Rua Marques * Vicente. 225-Gavea, 22453-900 Rio de Janeiro, RJ, Brazil
Abstract This paper proposes innovative concepts for shared state management of distributed virtual characters on a computer network. Firstly, visual soundness is presented as a property that is essential to high quality networked virtual environments and that is related to the autonomy of virtual components. Secondly, state consistency is defined in terms of physical and mental states and it is shown to be in opposition to visual soundness. Thirdly, the usual concept of dead reckoning is extended by exploring the idea of giving autonomy to remote replicas. This new concept, named goaloriented dead reckoning, is particularly advantageous for the state management of autonomous characters. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Distributed/network graphics; Collaborative virtual environment; Artificial life; Dead reckoning; Autonomous characters
1. Introduction Virtual environments distributed over computer networks began in the early 80s [1]. However, despite all these years of activities, this research area still faces enormous challenges and lacks uniformity in its concepts and definitions, specially considering networked virtual environments populated by ‘‘intelligent’’ characters. Notably, autonomy of virtual humans or A-Life entities is a subject not fully investigated in the context of shared state management. Unfortunately, at the other side of the spectrum, most of the people working on ‘‘intelligent’’ characters [2–8] are not focused on networked virtual environments. The present work reduces the gap between the subjects above mentioned and contributes to the discussion of artificial life over a large global computer *Corresponding author. ICAD-Intelligent CAD Laboratory, Department of Computing, PUC-Rio, Rua Marqu#es de S*ao Vicente, 225-Gavea, 22453-900 Rio de Janeiro, Brazil. Tel.: +55-21-2529-9544 x 4308; fax: +55-21-2259-2232. E-mail addresses: [email protected] (D. Szwarcman), ! [email protected] [email protected] (B. Feijo), (M. Costa).
network rather than inside a single computer. Also this work creates new possibilities for collective and massive human interaction in a networked A-Life environmentFa crucial issue for A-Life Games and A-Life Arts. This paper relies on the autonomy of virtual components’ remote replicas (clones) as an adequate approach to networked virtual environments. It presents a flexible and clear view of the concept of shared state management and proposes an extended form of dead reckoning as an appropriate technique to produce high quality virtual environments. In the context of the present work, autonomy is the capacity of the virtual component to react to events (reactivity), to act by its own and to take initiatives (pro-activity). Moreover, autonomy is a concept with no relation to representation; that is, an autonomous character may be an avatar, which represents a human participant, or it may be an A-Life entity that does not represent a user and is not directly controlled by him/her. In most of the current applications, autonomous characters living on a network do not have autonomous clones. However, depending on the degree of complexity of the autonomous characters and the number of hosts on the network, the lack of clone autonomy may render the virtual world unfeasible. Therefore, this paper claims
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that massive networked artificial life should be heavily based on clone autonomy, despite the problems of consistency that should be solved. Before presenting the main ideas of this paper, the most closely related works are described in Section 2. Sections 3 and 4 are dedicated to some notes on terminology and general concepts that are essential to understand the type of problem tackled in this paper. In Section 5, this paper proposes the idea of visual soundness, indicating it as measure of networked virtual environments quality and showing its dependence on virtual components autonomy. The same section presents the notion of physical and mental state consistency and its relation to visual soundness. Sections 6 and 7 explain in detail the proposed goal-oriented shared state management technique. In Section 8, the goal-oriented dead reckoning is then presented as a generalization of the current dead reckoning techniques. Finally, experimental results are described in Section 9 and conclusions are produced in Section 10.
2. Related work JackMOO [9], VLNET [10] and Improv [11] are remarkable works on distributed virtual actors. These works admit autonomy of actors’ remote replicas, but only at the level of fundamental physical actions such as step-forward or turn-around. In the works above mentioned, remote replicas are able to choose appropriate positions for their articulated parts to avoid immediate collision and guarantee equilibrium, but they cannot make higher level decisions such as deviating right or left. Thus, for an actor to cross a room, where several other characters move around, it must guide its remote replicas almost every step of the way. In this context, virtual actors are said to have ‘‘one mind and multiple bodies’’ [11]. Shared mental state is not considered in the literature. JackMOO [9] and VLNET [10] adopt centralized models that produce highly consistent virtual environments, where all participating hosts display nearly the same animation sequences. However, the central server can easily become the speed bottleneck. In JackMOO [9], the central server maintains a semantic state databaseFwith propositional information such as ‘‘actor 1 is standing’’Fwhile each client maintains a graphical state database replica. Then the server orders the execution of low-level physical actions at each client. VLNET authors do not clearly mention how autonomy is provided to virtual actors’ replicas. In Improv [11], an actor consists of the following parts: its geometry, an Animation Engine, which utilizes descriptions of atomic actions to manipulate the geometry, and a Behavior Engine that constitutes the actor’s mind. Improv’s distribution model replicates
the geometry and Animation Engine on every participating Local Area Network (LAN). The Behavioral Engine runs at a single LAN only. All body replicas move according to the one-mind directions. Each actor’s mind resides in a different LAN and thus the environment’s mental database is not concentrated in a single central server. A flaw in Improv is that the authors do not explicitly mention at what level consistency is maintained. If the environment is to be highly consistent, all Behavioral Engine hosts must follow a centralized model. That is, these hosts must exchange acknowledgements in order to guarantee the same sequence of actions at all computers. Again, a centralized model can easily turn into a speed bottleneck. If, on the other hand, consistency is not maintained at a high level, then each host may display environment changes in a different order. In this case, precision problems may occur, such as the body of an actor running through the body of another actor. Improv authors do not reveal how these problems are handled. The literature on shared state management for artificial life is practically missed, although some research on networked artificial life has been reported [12].
3. Distributed or networked VE? The terms ‘‘distributed virtual environment’’ or ‘‘distributed A-Life environment’’ do not emphasize the nature of the problems tackled in this paper. Distributed virtual environment is a broader concept to characterize systems where an application is decomposed into different tasks that are run on different machines or as separate processes. The designations ‘‘networked virtual environment’’ and ‘‘networked A-Life environment’’ are more specific and adequate terms to define a software system in which users or A-Life entities interact with each other in realtime over a computer network. This is precisely the definition given by Sandeep Singhal and Michael Zyda [1]. Sometimes the cumbersome term ‘‘distributed interactive simulation environments’’ is used in the literature. This is motivated by the DIS network software architecture and its successor HLA (HighLevel Architecture) [13], which was recently been nominated as an IEEE standard and an OMG standard.
4. Shared state management Shared state management is the area of networked virtual environments dedicated to the techniques for maintaining shared state information [1]. Since the primary concern of networked virtual environments is to give users the illusion of experiencing the same world,
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consistency of the shared information is a fundamental issue. However, it is hard to be achieved without degrading the virtual experience. In fact, one of the most important principles in networked virtual environments says that no technique can support both high update rate and absolute state consistency [1]. Total consistency means that all participants perceive exactly the same sequence of changes in the virtual environment. To guarantee such condition, the state of the virtual environment can only be modified after the last update has been surely disseminated to all hosts. For that matter, shared state information must be stored in a centralized repositoryFor some corresponding model [14]Fwith hosts exchanging acknowledgements and retransmitting lost state updates before proceeding with new ones. Hence, update rates become limited, which in turn produces non-smooth movements and non-realistic scenes. Highly dynamic virtual environments, such as the ones populated by autonomous characters, must tolerate inconsistencies in the shared state. There are two shared sate management techniques that allow inconsistencies [1]: (1) frequent state update notification; (2) dead reckoning. In the first technique, hosts frequently broadcast (or multicast) update changes without exchanging acknowledgments. This technique has been mostly used by PC-based multi-player games [15]. In dead reckoning techniques (see Section 6), the transmission of updates is less frequent than the occurred state changes and the remote hosts predict the objects’ intermediate states based on the last transmitted update. Both techniques allow the use of decentralized replicated databases where every virtual component has a pilot, the main representation that resides in its controlling host, and several clones, the replicas that reside in other hosts. The quality of a networked virtual environment is also determined by its scalability, which is its capacity of increasing the number of objects without degrading the system. Therefore, techniques for managing shared state updates should be used in conjunction with resource management techniques for scalability and performance. Singhal and Zyda [1] present an excellent description of some of these techniques, such as packet compression, packet aggregation, area-of-interest filtering, multicasting protocols, exploration of the human perceptual limitations (e.g. level-of-detail perception and temporal perception) and system architecture optimizations (e.g. server clusters and peer-server systems).
5. Visual soundness and state consistency In a networked virtual environment all participating hosts should pass the test of visual soundness, an important subjective test proposed in this paper. A host passes the test of visual soundness if the user is unable to
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distinguish between local and remote objects on the display. An environment that passes this test may be considered a networked virtual environment that is visually sound. The theoretical definition of the visual soundness concept presents the same sort of difficulties as the ones encountered, for example, by Artificial Intelligence researchers in defining an intelligent system. In these cases, subjective tests offer valuable contributions, providing operational definitions until a formal one becomes available. The test of visual soundness is, in this sense, analogous to the Turing Test [16], which continues to be a reference for intelligent systems in spite of being highly subjective. For a user to be unable to note the difference between local and remote virtual components, all of them must move smoothly and interact in a precise way (local precision). One example of local precision failure is the overlapping of objects. Another example is when a character fails to put his hand in the right place while trying to shake hands with another one. As already mentioned, in totally consistent environments hosts will most probably fail the visual soundness test because of limited update rates. In the case of environments that allow inconsistencies, large jumps and overlapping usually occurs when the clone tries to follow the pilot’s movement. Although, for the sake of visual soundness, inconsistency must be tolerated, it should be kept within certain limits. Moreover, being physically discrepant from its pilot, a clone can also turn out to express a different mood or emotional state. State consistency must be considered in a broader sense. A clone then exhibits state consistency when it mirrors the following states of its pilot: *
*
physical state (e.g. position, orientation, velocity, acceleration, etc.); mental state, including facts, beliefs, desires, intentions, commitments, goals, emotions, etc.
It is evident that a host may exhibit a high level of state consistency but fail the test of visual soundness (e.g. clones and their pilots have quite similar positions and velocities, but clones’ movements are jerky). The reverse is also possible; that is, a host may pass the test but exhibit low level of state consistency. In fact, state consistency and visual soundness can be understood as being inversely proportional to each other. State consistency is increased by making clones act like their pilots, that is, act like a remote object, which in turn decreases visual soundness. Looking the other way around, visual soundness is related to local precision, which is achieved by giving autonomy to clones, that is, capacity to act by their own according to the local environment presented by each host. This perspective based on the problem of autonomy is the starting point
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for the main ideas of this paper. A totally autonomous clone would act exactly like a local object but would, of course, have no state consistency. The challenge is to search for techniques that maintain physical and mental state consistency while passing the test of visual soundness and favoring scalability. The authors believe that dead reckoning is the basic technique to overcome the above-mentioned challenge. By considering the technique from the viewpoint of clone autonomy, the usual concept of dead reckoning can be expanded to take into account the states of mind of entities. In this manner, autonomous characters can be supported in a more adequate way.
6. Dead reckoning and clone autonomy Dead reckoning has its origin in networked military simulations and multi-player games, where users manipulate aircraft or ground vehicles. In these systems, prediction is a matter of trying to guess the pilot’s trajectory. Second-order polynomial is the technique most commonly used to estimate these objects’ future position based on current velocity, acceleration and position. It provides fairly good prediction since aircraft and vehicles do not usually present large changes in velocity and acceleration in short periods of time. Moreover, since the prediction algorithm is deterministic, the source host can calculate the error between pilot and clones’ positions, and send updates only when the inconsistency level is considered unacceptable. Upon receiving update messages, clones restore consistency by converging (or simply ‘‘jumping’’) to the pilot’s transmitted physical state. Convergence is usually done with curve fitting [1]. In this case, clones do not have any autonomy to deviate from obstacles. So, when their trajectories differ from the pilot’s real path, either during prediction or convergence, clones may overlap other objects. When one comes to articulated characters, a couple of different approaches can be taken for dead reckoning. For applications requiring high state consistency, jointlevel dead reckoning algorithms can be used to predict in-between postures [17]. This approach does not take into account the action the character is executing and dead reckoning computations are performed on joint angle information received at each update message. However, in many situations consistency at the level of articulated parts is not essential. Especially in collaborative work systems, where avatars interact with each other and together manipulate objects, visual soundness at each host becomes more important. Furthermore, articulated characters have gained autonomy over the past years and efforts are being made to give them capacity to understand and accept orders like ‘‘say hello to Mary’’ [9]. In this context, dead reckoning at the level
of physical actions has been considered [9–11, 18]. That is, instead of sending position updates, the pilot sends to clones messages that indicate the low-level physical action it is executing, like ‘‘smile’’, ‘‘dance’’ or ‘‘wave’’. Clones predict pilot’s states by performing the same actions, regardless of the fact that each host may be presenting different motion sequences. However, as already mentioned, in these script-based systems clones have very limited autonomy. Even though they are able to choose the appropriate place to put their foot when stepping forward, they cannot decide to deviate right or left from another avatar. Their pilots have to instruct them which way to take. Clones, in this case, make decisions based on their current perception of the environment because they do not keep previous perceptions. In fact, clones do not store facts or other mental attributes, and thus they lack a mental state. For overlapping not to occur, these systems must guarantee that certain actions will not be executed in parallel: one will only be started after the other has been finished at all hosts. For instance, two very close characters may run into each other if they are ordered to step forward at the same time. In other words, total physical state consistency must be achieved between certain actions. Visual soundness may reach undesirable levels. This paper proposes an extension to character dead reckoning that gives even more autonomy to clones, providing them with a mental state and, on top of that, greater decision power and planning capacity. With limited autonomy, clones are restricted to executing by their own only certain localized actions. For example, if the pilot tells them to ‘‘walk to the door’’, the environment will probably not remain still during all the time they take to get there. So, while they walk, the pilot must tell them how to respond to environment changesF‘‘deviate right’’ or ‘‘get your head down’’. However, remembering that pilot and clones always experience different views, some hosts may present poor visual soundness. On the other hand, if clones can make decisions based on what they ‘‘see’’, ‘‘hear’’ and ‘‘feel’’, then they can get to the door naturally, deviating from an angry dog or smiling to a friend that passes by. With greater autonomy, clones can receive not only high-level actions to execute but, in a broader sense, they can receive a goal to be achieved. The goal reflects the pilot’s final desired physical and/or mental state. Clones decide which sequence of actions, or prediction plan, to execute in order to accomplish the given goal. For instance, if the pilot sends the goal ‘‘outside the room’’, clones can decide which exit to take. Fig. 1 shows this particular example for a character that does not like Mr. Green, another avatar in the room. The pilot and its clone experience different emotional states depending on the position and posture of Mr. Green at each host; consequently, they choose different doors to go through. In spite of the fact that the local and remote hosts
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Fig. 1. Pilot and clones experience different emotional states.
exhibit different animated scenes, this distributed virtual experience will be perfectly acceptable if the character is given only the following properties: ‘‘leave the room’’ (goal) and ‘‘I am afraid of Mr. Green’’ (emotional state). The specified goal and the prediction plan executed by the clone constitute the prediction mechanism. Although positional goals are easier to conceive, it should be emphasized that a goal is not restricted to a final physical state. Clones can be ordered to accomplish a ‘‘not hungry’’ or a ‘‘very calm’’ state, which are strictly mental but might require the execution of high level actions such as buying food or practicing yoga. Due to the fact that pilot and clones have mental attributes, the autonomous character acquires a shared mental state.
Since clones are given more autonomy, they can take different amounts of time to achieve a given goal. A clone may then receive a second goal before it is able to accomplish the first one. However, all clones must start and end goal pursuits in the same order carried out by the pilot. For this reason, the prediction mechanism requires that pilots also inform clones of the achieved goals. The proposed management system for autonomous characters maintains goal consistency between pilot and clones; that is, state consistency is kept at the mental level. Consistency of the physical state and of other constituents of the mental state (emotions, beliefs, etc.) is a result of the type of goal given. The more restrictive the goal is, the stronger the final state consistency will
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be. It is evident that, during the execution of the prediction plan, clones might deviate from their pilots’ states. The pilot can control, to a certain extent, clones’ state consistency during prediction by specifying resources to be used. In the example of Fig. 1, the pilot could have told clones to go ‘‘outside the room through the right door’’. The more restrictive the resources are, the stronger the intermediate state consistency will be. In some situations, even with the specification of resources, different decisions can take clones’ physical and mental state consistency to unacceptable levels. In this matter, a recovery mechanism that restores state consistency is presented in the next section. It is worth noting that, for the existing dead reckoning protocols, inconsistencies introduce overlapping and interaction problems. Clones that have greater autonomy maintain local precision even when they are inconsistent.
their way to fulfill a goal, the average working recovery rate will tend to be low. The clone’s first attribution is to verify if it needs to recover when it receives the pilot’s messages. That will be the case if inconsistency exceeds the acceptable limit. One possible evaluation criterion is to consider the inconsistency level unacceptable when the clone is approximating neither the recovery goal nor the main goal. The clone will recover from inconsistencies by suspending the prediction plan and devising a recovery sequence of actions, or a recovery plan, that it will execute to achieve the given intermediate goal in the most natural way possible. When recovery is terminated, the clone takes on again the fulfillment of the main goalFconceiving and executing a new prediction plan. Fig. 2 illustrates the following case: *
7. Recovering mechanism keeps clones under control
*
Increasing clones’ autonomy makes them capable of responding to the environmentFavoiding collisions, expressing emotions and interacting with other charactersFaccording to what they experience locally. Each clone can act differently as long as it achieves the pilot’s goal. The prediction mechanism is such that the estimated states are not the same at all hosts and are unknown to the pilot. Taking the simple example where an avatar must get to a given position, while the pilot decides to deviate right from a fearing entity, one or more clones may decide to deviate left or even not deviate at all, depending on the entity’s state at each host. If all clones get to the desired position in approximately the same period of time, then the goal is achieved. However, clones that deviate left may encounter other obstacles that can make them get too far from the pilot and from the goal. In this case, these clones should try to recover to the pilot’s state. To restore state consistency, this paper proposes an original recovery mechanism. Both, pilot and clone, have specific roles in this mechanism. The pilot, after telling clones the goal to be achieved, should send them recovery messages until the desired final state is reached. Recovery messages specify recovery goals, which reflect the pilot’s intermediate physical and/or mental states. The interval of time between recovery messages may be determined by environment properties that influence clones state consistency, such as the type of application, the number of entities and the level of activity. In this way, the recovery rate will be dynamically adapted to environment demands. Since the pilot has no way of knowing how discrepant clones are, the recovery rate cannot be based on state error. Considering that recovery messages have the purpose of helping clones out in extraordinary situations where they cannot find
*
*
*
*
*
Avatar 1 is given the properties ‘‘leave the room’’ and ‘‘keep away from people’’. Pilot 1 sends the main goal ‘‘outside the room’’ to its clone (clone 1). Avatar 2 is swinging randomly around a small area, which causes different reactions from other objects (i.e. the hosts will never display the same position of avatar 2). Clone 1 initially goes left (because the left door is a valid exit), but it soon notices avatar 3 and moves away from the main goal to avoid the character. At point B, pilot 1 sends a recovery message informing of its present position (recovery goal). Clone 1 receives the recovery message when it is at point A. Recovery is triggered, since the clone is approximating neither ‘‘outside the room’’ nor point B. Clone 1 recovers from point A to point B.
At point B, clone 1 reconsiders the best door to go through and decides to follow the pilot’s path towards the right door. It could have decided for the left door again. In any case, the final desired goal would have been achieved. According to the suggested inconsistency evaluation criterion, recovery is only performed if the clone is approximating neither the main nor the recovery goal. Hence, it is not only necessary that state constituentsFphysical and mentalFbe quantified, but also that a clone be capable of computing how distant its own global state is from the final and intermediate desired states. In the implemented examples, states are considered to be vectors in a space whose dimensions correspond to physical and mental attributes. A clone then calculates how far it is from a desired state by performing multi-dimensional vector subtraction. Naturally, this approach requires a procedure for normalizing the various types of attribute measures to the same range of values, so that no vector coordinate outweighs any
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Fig. 2. Recovering state consistency.
other one. Such a procedure has not yet been fully investigated and should be completed in future works. Recovery actions are restrictive in the sense that they limit clones’ autonomy in order to compel them to achieve the recovery goal as fast as possible. However, a clone can receive a new recovery goal before it reaches the last one. In this case, the clone shifts its aim to the new goal, if recovery is still needed. Recovery can become as restrictive as a state update if a clone finds too much difficulty in accomplishing the recovery goal.
The set of attributes S t includes physical and mental situations, that is:
8. Dead reckoning generalized
Naturally, entities should also be able to execute plans. In the case of entities that have no decision power, such as a ball that is thrown, the plan is reduced to the application of a procedure, usually the physical law that describes the entity’s behavior. Action descriptors may be simple operators or conditional actions. Conditional actions may be relative to states or to other actions. In the occurrence of an event not predicted as a condition, re-planning takes place. Thus, the virtual component’s autonomy is established at the level of conditional actions and at the re-planning level. In this paper, conditional actions are restricted to simple conditions including only states, and plans are restricted to sequence of actions. The case of actions executed in parallel is not investigated.
Based on the prediction and recovery mechanisms presented in the last two sections, this paper proposes a generalization of the dead reckoning technique called goal-oriented dead reckoning. This generalization is grounded on the assumption that pilot and clones are completely specified by the following triple:
Oti ¼ Eit ; Sit Gti t ¼ time; where Eit is the set of entities that compose the environment inhabited by entity i, at time t; Sit the set of attributes that represent entity i, at time t; Gti the expression that defines the main goal of entity i, at time t.
t S t ¼ Sft ,Sm :
Another assumption is that entities are capable of devising plans: Pt ¼ pðE t ; St ; Gt Þ
or E t ; St ; Gt -pt ;
where a plan is a set of action descriptors pt ¼ at1 at2 ; y :
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The above assumptions, as well as the goal fulfillment situation, can all be described in some formal language as powerful as the mathematical first order logic. This formal description is not one of the main concerns of this paper, since theorems are not to be derived. However, for the purpose of clarity, the simplified assumptions and the situation of goal achievement, adopted in this work for the goal-oriented dead reckoning, are expressed in first order logic. In this way, the plan is a sequential list of actions, p ¼ ½a1 ; a2 ; y and the main goal Gt is a sentence in the normal disjunctive form, 8x1 ; 8x2 ; yGt ¼ b1 3b2 3y;
p=[move(MrBlue, 2.0, 0, 0), move(MrBlue, 2.0, 0, 3.0)] An example of a conditional action is: if (facing(MrBlue, danger), deviateRight(MrBlue), continue) That is, if Mr. Blue faces any danger, he deviates right, otherwise he continues. The formalization of a planner is not in the scope of this paper and its implications are discussed elsewhere [18]. The goal-oriented dead reckoning is presented in this paper in the form of a pseudo-language algorithm, which is listed in the Appendix. For a quicker understanding, Fig. 3 shows a graphical representation that summarizes the proposed generalization as follows: *
where xn is a variable and bk is a conjunction of literals, bk Lk1 4Lk2 4y : Literals, on the other hand, are predicates, negated or not, that may include constants, function symbols and variables. In this simplified case, the goal is achieved by an entity i, at time t0 , when the following relation holds:
*
0
( k such that fLk1 ; Lk2 ; ygDSit : Examples of a simple goal (without variables) and a simple plan are: G=(positionX(MrBlue, 2.0) 4 positionZ(MrBlue, 3.0)) 3 happy(MrBlue)
*
At time t0, the pilot sends its initial attributes and the main goal ðSi0 ; G0i Þ to clones. The pilot devises and executes its plan. Upon receiving the pilot’s messages, clones devise and execute prediction plans ðEj0 ; Sj0 ; G0i -p0j Þ: At some time t1, the pilot sends a recovery goal ðg1i Þ to clones. Upon receiving the recovery message, clones verify if inconsistency is greater then the acceptable limit ðd > dlim Þ: If a given clone finds such condition true, it suspends the prediction plan, devises a recovery plan (E1j , S1j , g1i -p1j .) and starts executing it. Otherwise, the clone continues without re-planning. At time t2, the recovering clone reaches the intermediate goal and takes on again the main goal,
Fig. 3. Graphical representation of goal-oriented dead reckoning.
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*
devising and executing another prediction plan ðEj2 ; Sj2 ; G2j -p2j Þ: At time t3, the pilot achieves the main goal and communicates the fact to clones. Upon receiving this information, clones start time-out counters. If a clone does not reach the main goal before time-out, then it will probably have its attributes updated to satisfy the given goal.
Traditional dead reckoning as well as joint angle and physical action dead reckoning are special cases of goaloriented dead reckoning. In other words, previous versions can be described in terms of prediction and recovery mechanisms, considering purely physical goals, of course. The traditional form is normally applied to vehicles that are guided by joysticks or similar devices. In this case, the final state cannot be predetermined and the main goal is actually unknown. Therefore, the main goal and achieved goal messages are never sent to clones. The prediction plan is reduced to the second order polynomial approximation, or some equivalent algorithm, that depends only on the entity’s present attributes. ðSjt -ptj Þ: Update messages are special cases of recovery messages where the transmitted recovery goals are purely physical states. The convergence algorithm, on the other hand, is a particular type of recovery plan that does not depend on the environment’s conditions ðSjt ; gtj -ptj Þ: In traditional dead reckoning, clones converge to the pilot’s state every time an update message is received. This is equivalent to setting the inconsistency limit to zero (dlim=0) in the goal-oriented dead reckoning algorithm. The same reasoning holds true for joint angle dead reckoning, which is traditional dead reckoning applied to the several parts of an articulated entity. In physical action dead reckoning, the main goal is given in the form of the action to execute in order to
achieve it. Clones never recover. This is, of course, a more restrictive form of goal-oriented dead reckoning where clones cannot choose the sequence of actions to perform and where recovery is not considered. The properties of the various types of dead reckoning are summarized in Table 1. The qualitative values (high, medium, low) presented in the table are not a result of theoretical calculations or empirical measurements; they are only given as indications of the potentialities of each type of dead reckoning. As a generalization, the goal-oriented dead reckoning allows the degree of autonomy to be adapted to virtual component type and to environment needs. In fact, even clones of non-autonomous characters can be given some autonomy to avoid overlapping.
9. Experimental results In order to implement prototypes and obtain experimental results, a framework for networked autonomous characters is being developed. The framework is built on top of Bamboo [19,20], an open-architecture language independent toolkit for developing dynamically extensible, real-time, networked applications. Bamboo’s design focuses on the ability of the system to configure itself dynamically, which allows applications to take on new functionality after execution. Bamboo is particularly useful in the development of scalable virtual environments on the Internet because it facilitates the discovery of virtual components on the network at runtime. The framework for distributed autonomous characters offers a set of classes that provides the basic functionality needed for shared state management using
Table 1 Comparison of the various dead reckoning types Traditional dead reckoning Main goal Recovery goal Prediction plan Recovery plan Minimum recovery rate Consistency Maximum visual soundness Maximum clone autonomy
Joint-angle dead reckoning
Physical action dead reckoning
Goal-oriented dead reckoning
Physical (unknown) Physical Polynomial Convergence algorithm Medium (based on state error) Physical (high) Medium
Physical Physical Kalman filtering State update
Physical F Action scripts F
Physical/mental Physical/mental Devised by clone Devised by clone
High (based on state error) Physical (high) Medium
F Physical (medium to high) Medium
Low (based on environment) Goal (low to high) High
None
None
Medium
High
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goal-oriented dead reckoning. It also supports frequent state regeneration and previous versions of dead reckoning as special cases. Several examples were run in a Windows NT environment producing good results, although performance was not formally investigated. Two of these examples are shown in Figs. 1 and 2. Fig. 4 illustrates the case where two charactersFMr. and Mrs. BlueFare ordered to achieve the ‘‘very happy’’ state, a purely mental goal. Mr. and Mrs. Blue become happier as they approximate each other. When they get close, they both demonstrate to be very happy by raising their arms. Since the goal does not specify physical attributes, the final positions of Mr. and Mrs. Blue are different at each host.
10. Conclusions and future work Autonomy of virtual humans or A-Life entities is not a well-explored concept in the area of networked virtual environments. On the other hand, most of the people working on autonomous characters are not focused on shared state management over a computer network. This paper explores a common view amongst these areas of research and proposes innovative concepts for shared state management. Firstly, state consistency is defined in terms of physical and mental states. Secondly, visual soundnessFa concept ‘‘inversely proportional’’ to state consistencyFis associated with the autonomy of clones (regardless of the pilot’s natureFa user or an autonomous creature). Finally, a new extension to the concept of dead reckoning is proposed, which is particularly advantageous for autonomous characters. The advantages and worries of this new approach, called goal-oriented dead reckoning, are presented below. Although one has already mentioned that state prediction may be object-specific and dead reckoning should handle any type of shared state [1], these two ideas are not clearly explained in the literature. For instance, the authors of Improv [11] do not present this system in terms of shared state management, although its scripted events have been recognized as a form of object-specific state prediction elsewhere [1]. JackMOO [9] and VLNET [10] are also impressive works on distributed virtual actors but in these systems, like in Improv, clones have very limited autonomy. As far as collision is concerned, real-time distributed collision agreement for dead reckoning algorithms are not clearly mentioned in the literature [1]; and the existent systems probably fail the test of visual soundness. Most of the interesting dead reckoning extensions, such as the position history-based protocol [21], produce smooth animation but cannot cope with undesirable collisions that happen when the predicted trajectory differs from
the real one. One possible solution for many of the above-mentioned drawbacks is to relax the concept of dead reckoning by exploring the idea of autonomy. Therefore, this paper proposes the goal-oriented dead reckoning, which allows clones to be empowered by autonomy in order to produce visually sound networked virtual environments. The proposed shared state management mechanism also works in favor of scalability in the sense that it tends to substantially reduce the number of transmitted network messages. Moreover, it is expected that the contributions of the goal-oriented dead reckoning, with respect to scalability, become more significant as mental models are improved, since clones will tend to need less instructions from the pilot. It should be mentioned that, like physical action dead reckoning, the goal-oriented dead reckoning requires that special care be given to the interaction between autonomous characters and moving objects. If, for example, while ‘‘Mr. Green’’ is dancing, a ball is thrown, it might hit ‘‘him’’ at one host and not hit ‘‘him’’ at another host. Either the ball’s trajectory is allowed to be different at each host, which will most likely make pilot and clones end up at different positions, or the ball is forced to always follow its pilot’s path, which will probably produce unrealistic scenes. As compared with previous versions, which are just special cases, the generalized prediction and recovery mechanisms are, of course, harder to program. A greater share of each host’s computational capacity is also needed. Clones that possess autonomyFas in physical action or goal-oriented dead reckoningFrequire a more elaborate initialization. When a new participant joins the environment, existing actors may be executing an action or fulfilling a goal. Therefore, initialization of clones at the new user’s host cannot be done with a simple state update. If, to increase scalability, the environment is divided in areas of interest associated to multicast groups, then clones must be created and initialized as soon as the pilot crosses region boundaries. There are several topics for future work. For instance, performance of the goal-oriented dead reckoning and its impact on scalability should be formally investigated. Furthermore, a complexity analysis of the proposed algorithm is required. Recovering criteria should also be studied in more detailsFon the pilot’s side, this concerns the set of rules used to dynamically adapt the recovery message rate; on the clones’ side, the focus is on the set of rules used to trigger recovery. Complex conditional actions and complex plans including parallel actions should also be considered. Moreover, resource management techniques for scalability need to be carefully examined. Real-time distributed collision agreement is another important issue for further research.
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Fig. 4. Mr. and Mrs. Blue go after the ‘‘very happy’’ state.
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Acknowledgements The authors would like to thank the CNPq for the financial support. Appendix .oal oriented dead reckonin. { vector/planS plans of pilot[MAX PILOTS]; vector/planS plans of clone[MAX CLONES]; vector/goalS main goals of clone[MAX CLONES]; inconsistency d[MAX CLONES]; const inconsistency dlim[MAX CLONES]; /* prediction and recovery mechanisms while (true) for (each pilot Pi) /*prediction: send main goals and achieved goals if (new goal Gti ) send reliable message with goal Gti to clones; Eti ; Sti ; Gti -pti ; insert pti in plans of pilot[i]; endif for (each pk in plans of pilot[i]) execute a plan step; if (Sti satisfies pk’s goal) send reliably to clones ‘‘Gk achieved’’; erase pk; endif endfor /*recovery: send recovery messages if ((plans of pilot[i] is not empty) and (recovery message is needed)) send recovery goal gti to clones; endif endfor for (each clone Cj) if (new message) /*prediction: treat main goal if (message carries main goal G) if (no main goal is on timeout count) insert G in main goals of clone[j]; if (recovery plan is not running) Etj ; Stj ; G-ptj ; put ptj in plans of clone[j]; endif indicates that message has been treated; endif /*prediction: treat achieved goal else if (message is of type ‘‘G achieved’’) if (G is in main goals of clone[j]) start timeout counter for G; indicates that message has been treated;
/*recovery: treat recovery messages else if (message carries recovery goal g) if ðd½j > dlim ½jÞ empty plans of clone[j] Etj ; Stj ; g-ptj ; mark ptj as recovery plan; insert ptj in plans of clone[j]; else if (recovery plan is running) erase recovery plan; for (each Gk in main goals of clone[j]) Etj ; Stj ; Gk -ptj ; insert ptj in plans of clone[j]; endfor endif indicates that message has been treated; endif endif /*prediction and recovery: plan execution for (each pk in plans of clone[j]) execute a plan step; if (Stj satisfies pk’s goal) if (pk is the recovery plan) for (each Gn in main goals of clone[j]) Etj ; Stj ; Gn -ptj ; insert ptj in plans of clone[j]; endfor else erase pk’s goal from main goals of clone[j]; endif erase pk; endif endfor /*prediction: goal timeout for (each Gk in main goals of clone[j]) if (timeout) erase pk from plans of clone[j]; force Stj to satisfy Gk; erase Gk; endif endfor endfor endwhile }
References [1] Singhal S, Zyda M. Networked virtual environmentsFdesign and implementation. New York: ACM Press, 1999. [2] Reynolds CW. Flocks, herds, and schools: a distributed behavioral model. Computer Graphics (Proceedings of SIGGRAPH) 1987;21(4):25–34. [3] Bates J, Loyall AB, Reilly WS. Integrating reactivity, goals, and emotion in a broad agent. Technical Report CMU-CS-92-144, School of Computer Science, CarnegieMellon University, Pittsburgh, PA, 1992.
D. Szwarcman et al. / Computers & Graphics 25 (2001) 999–1011 [4] Tu X, Terzopoulos D. Artificial fishes: physics, locomotion, perception, behavior. Computer Graphics (Proceedings of SIGGRAPH) 1994;28(3): 43–50. [5] Costa M, Feijo! B. Agents with emotions in behavioral animation. Computer & Graphics 1996;20(3):377–84. [6] Maldonado H, Picard A, Doyle P, Hayes-Roth B. Tigrito: a multi-mode interactive improvisational agent. Stanford Knowledge Systems Laboratory Report KSL-97-08, Stanford University, 1997. [7] Badler N, Bindiganavale R, Bourne J, Palmer M, Shi J, Schuler W. A parameterized action representation for virtual human agents. In: Proceedings of First Workshop on Embodied Conversational Characters (WECC 1998), Lake Tahoe, CA, 1998. [8] B!eicheiraz P, Thalmann D. A behavioral animation system for autonomous actors personified by emotions. In: Proceedings of First Workshop on Embodied Conversational Characters (WECC 1998), Lake Tahoe, CA, 1998. [9] Shi J, Smith TJ, Granieri J, Badler NI. Smart avatars in JackMOO. In: Proceedings of the 1999 Virtual Reality Conference (VR’99), IEEE, Texas, USA, 1999. p. 156–63. [10] Capin TK, Pandzic IS, Thalmann NM, Thalmann D. Realistic avatars and autonomous virtual humans in VLNET networked virtual environments. In: Earnshaw R, Vince J, editors. Virtual worlds in the internet. CA: IEEE Computer Society Press, 1998. p. 157–74 ([Chapter 8]). [11] Perlin K, Goldberg T. Improv: a system for scripting interactive actors in virtual worlds. In: Proceedings of SIGGRAPH’96, New Orleans, LA, 1996. p. 205–16. [12] Cliff D, Grand S. The creatures global digital ecosystem. Artificial Life, Winter 1999;5(1):77–93.
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[13] Dahmann J, Weatherly R, Kuhl F. Creating computer simulation systems: an introduction to the high level architecture. NJ: Prentice-Hall, 1999. [14] Anupam V, Bajaj C. Distributed and collaborative visualization. IEEE Multimedia 1994;1(2):39–49. [15] Doom.idSoftwareWebsite, http://www.idsoftware.com/ (as in August 28, 2000). [16] Turing AM. Computing machinery and intelligence. Mind 1950;59:433–560. [17] Capin TK, Pandzic IS, Thalmann D, Thalmann NM. A dead-reckoning algorithm for virtual human figures. In: Proceedings of the 1997 Virtual Reality Annual International Symposium (VRAIS’97), IEEE, Albuquerque, USA, 1997. p. 161–9. [18] Living Worlds Web site, http://www.vrml.org/WorkingGroups/living-worlds/draft 2/ (as in April 5, 2000). [19] Watsen K, Zyda M. BambooFa portable system for dynamically extensible, networked, real-time, virtual environments. In: Proceedings of the 1998 Virtual Reality Annual International Symposium (VRAIS’98), IEEE, Atlanta, GA, 1998. p. 252–9. [20] Bamboo Web site, http://npsnet.org/Bwatsen/Bamboo/ index.html (as in August 28, 2000). [21] Singhal SK, Cheriton DR. Using a position history-based protocol for distributed object visualization. In: Designing real-time graphics for entertainment. Course Notes for SIGGRAPH’94, Course #14, July 1994 [Chapter 10]. Also available as Technical Report STAN-CS-TR-1505, Department of Computer Science, Stanford University, 1994.
Goal-oriented dead reckoning for autonomous characters ! M. Costa D. Szwarcman, B. Feijo*, # de Sao ICAD-Intelligent CAD Laboratory, Department of Informatics, PUC-Rio, Rua Marques * Vicente. 225-Gavea, 22453-900 Rio de Janeiro, RJ, Brazil
Abstract This paper proposes innovative concepts for shared state management of distributed virtual characters on a computer network. Firstly, visual soundness is presented as a property that is essential to high quality networked virtual environments and that is related to the autonomy of virtual components. Secondly, state consistency is defined in terms of physical and mental states and it is shown to be in opposition to visual soundness. Thirdly, the usual concept of dead reckoning is extended by exploring the idea of giving autonomy to remote replicas. This new concept, named goaloriented dead reckoning, is particularly advantageous for the state management of autonomous characters. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Distributed/network graphics; Collaborative virtual environment; Artificial life; Dead reckoning; Autonomous characters
1. Introduction Virtual environments distributed over computer networks began in the early 80s [1]. However, despite all these years of activities, this research area still faces enormous challenges and lacks uniformity in its concepts and definitions, specially considering networked virtual environments populated by ‘‘intelligent’’ characters. Notably, autonomy of virtual humans or A-Life entities is a subject not fully investigated in the context of shared state management. Unfortunately, at the other side of the spectrum, most of the people working on ‘‘intelligent’’ characters [2–8] are not focused on networked virtual environments. The present work reduces the gap between the subjects above mentioned and contributes to the discussion of artificial life over a large global computer *Corresponding author. ICAD-Intelligent CAD Laboratory, Department of Computing, PUC-Rio, Rua Marqu#es de S*ao Vicente, 225-Gavea, 22453-900 Rio de Janeiro, Brazil. Tel.: +55-21-2529-9544 x 4308; fax: +55-21-2259-2232. E-mail addresses: [email protected] (D. Szwarcman), ! [email protected] [email protected] (B. Feijo), (M. Costa).
network rather than inside a single computer. Also this work creates new possibilities for collective and massive human interaction in a networked A-Life environmentFa crucial issue for A-Life Games and A-Life Arts. This paper relies on the autonomy of virtual components’ remote replicas (clones) as an adequate approach to networked virtual environments. It presents a flexible and clear view of the concept of shared state management and proposes an extended form of dead reckoning as an appropriate technique to produce high quality virtual environments. In the context of the present work, autonomy is the capacity of the virtual component to react to events (reactivity), to act by its own and to take initiatives (pro-activity). Moreover, autonomy is a concept with no relation to representation; that is, an autonomous character may be an avatar, which represents a human participant, or it may be an A-Life entity that does not represent a user and is not directly controlled by him/her. In most of the current applications, autonomous characters living on a network do not have autonomous clones. However, depending on the degree of complexity of the autonomous characters and the number of hosts on the network, the lack of clone autonomy may render the virtual world unfeasible. Therefore, this paper claims
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that massive networked artificial life should be heavily based on clone autonomy, despite the problems of consistency that should be solved. Before presenting the main ideas of this paper, the most closely related works are described in Section 2. Sections 3 and 4 are dedicated to some notes on terminology and general concepts that are essential to understand the type of problem tackled in this paper. In Section 5, this paper proposes the idea of visual soundness, indicating it as measure of networked virtual environments quality and showing its dependence on virtual components autonomy. The same section presents the notion of physical and mental state consistency and its relation to visual soundness. Sections 6 and 7 explain in detail the proposed goal-oriented shared state management technique. In Section 8, the goal-oriented dead reckoning is then presented as a generalization of the current dead reckoning techniques. Finally, experimental results are described in Section 9 and conclusions are produced in Section 10.
2. Related work JackMOO [9], VLNET [10] and Improv [11] are remarkable works on distributed virtual actors. These works admit autonomy of actors’ remote replicas, but only at the level of fundamental physical actions such as step-forward or turn-around. In the works above mentioned, remote replicas are able to choose appropriate positions for their articulated parts to avoid immediate collision and guarantee equilibrium, but they cannot make higher level decisions such as deviating right or left. Thus, for an actor to cross a room, where several other characters move around, it must guide its remote replicas almost every step of the way. In this context, virtual actors are said to have ‘‘one mind and multiple bodies’’ [11]. Shared mental state is not considered in the literature. JackMOO [9] and VLNET [10] adopt centralized models that produce highly consistent virtual environments, where all participating hosts display nearly the same animation sequences. However, the central server can easily become the speed bottleneck. In JackMOO [9], the central server maintains a semantic state databaseFwith propositional information such as ‘‘actor 1 is standing’’Fwhile each client maintains a graphical state database replica. Then the server orders the execution of low-level physical actions at each client. VLNET authors do not clearly mention how autonomy is provided to virtual actors’ replicas. In Improv [11], an actor consists of the following parts: its geometry, an Animation Engine, which utilizes descriptions of atomic actions to manipulate the geometry, and a Behavior Engine that constitutes the actor’s mind. Improv’s distribution model replicates
the geometry and Animation Engine on every participating Local Area Network (LAN). The Behavioral Engine runs at a single LAN only. All body replicas move according to the one-mind directions. Each actor’s mind resides in a different LAN and thus the environment’s mental database is not concentrated in a single central server. A flaw in Improv is that the authors do not explicitly mention at what level consistency is maintained. If the environment is to be highly consistent, all Behavioral Engine hosts must follow a centralized model. That is, these hosts must exchange acknowledgements in order to guarantee the same sequence of actions at all computers. Again, a centralized model can easily turn into a speed bottleneck. If, on the other hand, consistency is not maintained at a high level, then each host may display environment changes in a different order. In this case, precision problems may occur, such as the body of an actor running through the body of another actor. Improv authors do not reveal how these problems are handled. The literature on shared state management for artificial life is practically missed, although some research on networked artificial life has been reported [12].
3. Distributed or networked VE? The terms ‘‘distributed virtual environment’’ or ‘‘distributed A-Life environment’’ do not emphasize the nature of the problems tackled in this paper. Distributed virtual environment is a broader concept to characterize systems where an application is decomposed into different tasks that are run on different machines or as separate processes. The designations ‘‘networked virtual environment’’ and ‘‘networked A-Life environment’’ are more specific and adequate terms to define a software system in which users or A-Life entities interact with each other in realtime over a computer network. This is precisely the definition given by Sandeep Singhal and Michael Zyda [1]. Sometimes the cumbersome term ‘‘distributed interactive simulation environments’’ is used in the literature. This is motivated by the DIS network software architecture and its successor HLA (HighLevel Architecture) [13], which was recently been nominated as an IEEE standard and an OMG standard.
4. Shared state management Shared state management is the area of networked virtual environments dedicated to the techniques for maintaining shared state information [1]. Since the primary concern of networked virtual environments is to give users the illusion of experiencing the same world,
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consistency of the shared information is a fundamental issue. However, it is hard to be achieved without degrading the virtual experience. In fact, one of the most important principles in networked virtual environments says that no technique can support both high update rate and absolute state consistency [1]. Total consistency means that all participants perceive exactly the same sequence of changes in the virtual environment. To guarantee such condition, the state of the virtual environment can only be modified after the last update has been surely disseminated to all hosts. For that matter, shared state information must be stored in a centralized repositoryFor some corresponding model [14]Fwith hosts exchanging acknowledgements and retransmitting lost state updates before proceeding with new ones. Hence, update rates become limited, which in turn produces non-smooth movements and non-realistic scenes. Highly dynamic virtual environments, such as the ones populated by autonomous characters, must tolerate inconsistencies in the shared state. There are two shared sate management techniques that allow inconsistencies [1]: (1) frequent state update notification; (2) dead reckoning. In the first technique, hosts frequently broadcast (or multicast) update changes without exchanging acknowledgments. This technique has been mostly used by PC-based multi-player games [15]. In dead reckoning techniques (see Section 6), the transmission of updates is less frequent than the occurred state changes and the remote hosts predict the objects’ intermediate states based on the last transmitted update. Both techniques allow the use of decentralized replicated databases where every virtual component has a pilot, the main representation that resides in its controlling host, and several clones, the replicas that reside in other hosts. The quality of a networked virtual environment is also determined by its scalability, which is its capacity of increasing the number of objects without degrading the system. Therefore, techniques for managing shared state updates should be used in conjunction with resource management techniques for scalability and performance. Singhal and Zyda [1] present an excellent description of some of these techniques, such as packet compression, packet aggregation, area-of-interest filtering, multicasting protocols, exploration of the human perceptual limitations (e.g. level-of-detail perception and temporal perception) and system architecture optimizations (e.g. server clusters and peer-server systems).
5. Visual soundness and state consistency In a networked virtual environment all participating hosts should pass the test of visual soundness, an important subjective test proposed in this paper. A host passes the test of visual soundness if the user is unable to
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distinguish between local and remote objects on the display. An environment that passes this test may be considered a networked virtual environment that is visually sound. The theoretical definition of the visual soundness concept presents the same sort of difficulties as the ones encountered, for example, by Artificial Intelligence researchers in defining an intelligent system. In these cases, subjective tests offer valuable contributions, providing operational definitions until a formal one becomes available. The test of visual soundness is, in this sense, analogous to the Turing Test [16], which continues to be a reference for intelligent systems in spite of being highly subjective. For a user to be unable to note the difference between local and remote virtual components, all of them must move smoothly and interact in a precise way (local precision). One example of local precision failure is the overlapping of objects. Another example is when a character fails to put his hand in the right place while trying to shake hands with another one. As already mentioned, in totally consistent environments hosts will most probably fail the visual soundness test because of limited update rates. In the case of environments that allow inconsistencies, large jumps and overlapping usually occurs when the clone tries to follow the pilot’s movement. Although, for the sake of visual soundness, inconsistency must be tolerated, it should be kept within certain limits. Moreover, being physically discrepant from its pilot, a clone can also turn out to express a different mood or emotional state. State consistency must be considered in a broader sense. A clone then exhibits state consistency when it mirrors the following states of its pilot: *
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physical state (e.g. position, orientation, velocity, acceleration, etc.); mental state, including facts, beliefs, desires, intentions, commitments, goals, emotions, etc.
It is evident that a host may exhibit a high level of state consistency but fail the test of visual soundness (e.g. clones and their pilots have quite similar positions and velocities, but clones’ movements are jerky). The reverse is also possible; that is, a host may pass the test but exhibit low level of state consistency. In fact, state consistency and visual soundness can be understood as being inversely proportional to each other. State consistency is increased by making clones act like their pilots, that is, act like a remote object, which in turn decreases visual soundness. Looking the other way around, visual soundness is related to local precision, which is achieved by giving autonomy to clones, that is, capacity to act by their own according to the local environment presented by each host. This perspective based on the problem of autonomy is the starting point
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for the main ideas of this paper. A totally autonomous clone would act exactly like a local object but would, of course, have no state consistency. The challenge is to search for techniques that maintain physical and mental state consistency while passing the test of visual soundness and favoring scalability. The authors believe that dead reckoning is the basic technique to overcome the above-mentioned challenge. By considering the technique from the viewpoint of clone autonomy, the usual concept of dead reckoning can be expanded to take into account the states of mind of entities. In this manner, autonomous characters can be supported in a more adequate way.
6. Dead reckoning and clone autonomy Dead reckoning has its origin in networked military simulations and multi-player games, where users manipulate aircraft or ground vehicles. In these systems, prediction is a matter of trying to guess the pilot’s trajectory. Second-order polynomial is the technique most commonly used to estimate these objects’ future position based on current velocity, acceleration and position. It provides fairly good prediction since aircraft and vehicles do not usually present large changes in velocity and acceleration in short periods of time. Moreover, since the prediction algorithm is deterministic, the source host can calculate the error between pilot and clones’ positions, and send updates only when the inconsistency level is considered unacceptable. Upon receiving update messages, clones restore consistency by converging (or simply ‘‘jumping’’) to the pilot’s transmitted physical state. Convergence is usually done with curve fitting [1]. In this case, clones do not have any autonomy to deviate from obstacles. So, when their trajectories differ from the pilot’s real path, either during prediction or convergence, clones may overlap other objects. When one comes to articulated characters, a couple of different approaches can be taken for dead reckoning. For applications requiring high state consistency, jointlevel dead reckoning algorithms can be used to predict in-between postures [17]. This approach does not take into account the action the character is executing and dead reckoning computations are performed on joint angle information received at each update message. However, in many situations consistency at the level of articulated parts is not essential. Especially in collaborative work systems, where avatars interact with each other and together manipulate objects, visual soundness at each host becomes more important. Furthermore, articulated characters have gained autonomy over the past years and efforts are being made to give them capacity to understand and accept orders like ‘‘say hello to Mary’’ [9]. In this context, dead reckoning at the level
of physical actions has been considered [9–11, 18]. That is, instead of sending position updates, the pilot sends to clones messages that indicate the low-level physical action it is executing, like ‘‘smile’’, ‘‘dance’’ or ‘‘wave’’. Clones predict pilot’s states by performing the same actions, regardless of the fact that each host may be presenting different motion sequences. However, as already mentioned, in these script-based systems clones have very limited autonomy. Even though they are able to choose the appropriate place to put their foot when stepping forward, they cannot decide to deviate right or left from another avatar. Their pilots have to instruct them which way to take. Clones, in this case, make decisions based on their current perception of the environment because they do not keep previous perceptions. In fact, clones do not store facts or other mental attributes, and thus they lack a mental state. For overlapping not to occur, these systems must guarantee that certain actions will not be executed in parallel: one will only be started after the other has been finished at all hosts. For instance, two very close characters may run into each other if they are ordered to step forward at the same time. In other words, total physical state consistency must be achieved between certain actions. Visual soundness may reach undesirable levels. This paper proposes an extension to character dead reckoning that gives even more autonomy to clones, providing them with a mental state and, on top of that, greater decision power and planning capacity. With limited autonomy, clones are restricted to executing by their own only certain localized actions. For example, if the pilot tells them to ‘‘walk to the door’’, the environment will probably not remain still during all the time they take to get there. So, while they walk, the pilot must tell them how to respond to environment changesF‘‘deviate right’’ or ‘‘get your head down’’. However, remembering that pilot and clones always experience different views, some hosts may present poor visual soundness. On the other hand, if clones can make decisions based on what they ‘‘see’’, ‘‘hear’’ and ‘‘feel’’, then they can get to the door naturally, deviating from an angry dog or smiling to a friend that passes by. With greater autonomy, clones can receive not only high-level actions to execute but, in a broader sense, they can receive a goal to be achieved. The goal reflects the pilot’s final desired physical and/or mental state. Clones decide which sequence of actions, or prediction plan, to execute in order to accomplish the given goal. For instance, if the pilot sends the goal ‘‘outside the room’’, clones can decide which exit to take. Fig. 1 shows this particular example for a character that does not like Mr. Green, another avatar in the room. The pilot and its clone experience different emotional states depending on the position and posture of Mr. Green at each host; consequently, they choose different doors to go through. In spite of the fact that the local and remote hosts
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Fig. 1. Pilot and clones experience different emotional states.
exhibit different animated scenes, this distributed virtual experience will be perfectly acceptable if the character is given only the following properties: ‘‘leave the room’’ (goal) and ‘‘I am afraid of Mr. Green’’ (emotional state). The specified goal and the prediction plan executed by the clone constitute the prediction mechanism. Although positional goals are easier to conceive, it should be emphasized that a goal is not restricted to a final physical state. Clones can be ordered to accomplish a ‘‘not hungry’’ or a ‘‘very calm’’ state, which are strictly mental but might require the execution of high level actions such as buying food or practicing yoga. Due to the fact that pilot and clones have mental attributes, the autonomous character acquires a shared mental state.
Since clones are given more autonomy, they can take different amounts of time to achieve a given goal. A clone may then receive a second goal before it is able to accomplish the first one. However, all clones must start and end goal pursuits in the same order carried out by the pilot. For this reason, the prediction mechanism requires that pilots also inform clones of the achieved goals. The proposed management system for autonomous characters maintains goal consistency between pilot and clones; that is, state consistency is kept at the mental level. Consistency of the physical state and of other constituents of the mental state (emotions, beliefs, etc.) is a result of the type of goal given. The more restrictive the goal is, the stronger the final state consistency will
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be. It is evident that, during the execution of the prediction plan, clones might deviate from their pilots’ states. The pilot can control, to a certain extent, clones’ state consistency during prediction by specifying resources to be used. In the example of Fig. 1, the pilot could have told clones to go ‘‘outside the room through the right door’’. The more restrictive the resources are, the stronger the intermediate state consistency will be. In some situations, even with the specification of resources, different decisions can take clones’ physical and mental state consistency to unacceptable levels. In this matter, a recovery mechanism that restores state consistency is presented in the next section. It is worth noting that, for the existing dead reckoning protocols, inconsistencies introduce overlapping and interaction problems. Clones that have greater autonomy maintain local precision even when they are inconsistent.
their way to fulfill a goal, the average working recovery rate will tend to be low. The clone’s first attribution is to verify if it needs to recover when it receives the pilot’s messages. That will be the case if inconsistency exceeds the acceptable limit. One possible evaluation criterion is to consider the inconsistency level unacceptable when the clone is approximating neither the recovery goal nor the main goal. The clone will recover from inconsistencies by suspending the prediction plan and devising a recovery sequence of actions, or a recovery plan, that it will execute to achieve the given intermediate goal in the most natural way possible. When recovery is terminated, the clone takes on again the fulfillment of the main goalFconceiving and executing a new prediction plan. Fig. 2 illustrates the following case: *
7. Recovering mechanism keeps clones under control
*
Increasing clones’ autonomy makes them capable of responding to the environmentFavoiding collisions, expressing emotions and interacting with other charactersFaccording to what they experience locally. Each clone can act differently as long as it achieves the pilot’s goal. The prediction mechanism is such that the estimated states are not the same at all hosts and are unknown to the pilot. Taking the simple example where an avatar must get to a given position, while the pilot decides to deviate right from a fearing entity, one or more clones may decide to deviate left or even not deviate at all, depending on the entity’s state at each host. If all clones get to the desired position in approximately the same period of time, then the goal is achieved. However, clones that deviate left may encounter other obstacles that can make them get too far from the pilot and from the goal. In this case, these clones should try to recover to the pilot’s state. To restore state consistency, this paper proposes an original recovery mechanism. Both, pilot and clone, have specific roles in this mechanism. The pilot, after telling clones the goal to be achieved, should send them recovery messages until the desired final state is reached. Recovery messages specify recovery goals, which reflect the pilot’s intermediate physical and/or mental states. The interval of time between recovery messages may be determined by environment properties that influence clones state consistency, such as the type of application, the number of entities and the level of activity. In this way, the recovery rate will be dynamically adapted to environment demands. Since the pilot has no way of knowing how discrepant clones are, the recovery rate cannot be based on state error. Considering that recovery messages have the purpose of helping clones out in extraordinary situations where they cannot find
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*
*
Avatar 1 is given the properties ‘‘leave the room’’ and ‘‘keep away from people’’. Pilot 1 sends the main goal ‘‘outside the room’’ to its clone (clone 1). Avatar 2 is swinging randomly around a small area, which causes different reactions from other objects (i.e. the hosts will never display the same position of avatar 2). Clone 1 initially goes left (because the left door is a valid exit), but it soon notices avatar 3 and moves away from the main goal to avoid the character. At point B, pilot 1 sends a recovery message informing of its present position (recovery goal). Clone 1 receives the recovery message when it is at point A. Recovery is triggered, since the clone is approximating neither ‘‘outside the room’’ nor point B. Clone 1 recovers from point A to point B.
At point B, clone 1 reconsiders the best door to go through and decides to follow the pilot’s path towards the right door. It could have decided for the left door again. In any case, the final desired goal would have been achieved. According to the suggested inconsistency evaluation criterion, recovery is only performed if the clone is approximating neither the main nor the recovery goal. Hence, it is not only necessary that state constituentsFphysical and mentalFbe quantified, but also that a clone be capable of computing how distant its own global state is from the final and intermediate desired states. In the implemented examples, states are considered to be vectors in a space whose dimensions correspond to physical and mental attributes. A clone then calculates how far it is from a desired state by performing multi-dimensional vector subtraction. Naturally, this approach requires a procedure for normalizing the various types of attribute measures to the same range of values, so that no vector coordinate outweighs any
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Fig. 2. Recovering state consistency.
other one. Such a procedure has not yet been fully investigated and should be completed in future works. Recovery actions are restrictive in the sense that they limit clones’ autonomy in order to compel them to achieve the recovery goal as fast as possible. However, a clone can receive a new recovery goal before it reaches the last one. In this case, the clone shifts its aim to the new goal, if recovery is still needed. Recovery can become as restrictive as a state update if a clone finds too much difficulty in accomplishing the recovery goal.
The set of attributes S t includes physical and mental situations, that is:
8. Dead reckoning generalized
Naturally, entities should also be able to execute plans. In the case of entities that have no decision power, such as a ball that is thrown, the plan is reduced to the application of a procedure, usually the physical law that describes the entity’s behavior. Action descriptors may be simple operators or conditional actions. Conditional actions may be relative to states or to other actions. In the occurrence of an event not predicted as a condition, re-planning takes place. Thus, the virtual component’s autonomy is established at the level of conditional actions and at the re-planning level. In this paper, conditional actions are restricted to simple conditions including only states, and plans are restricted to sequence of actions. The case of actions executed in parallel is not investigated.
Based on the prediction and recovery mechanisms presented in the last two sections, this paper proposes a generalization of the dead reckoning technique called goal-oriented dead reckoning. This generalization is grounded on the assumption that pilot and clones are completely specified by the following triple:
Oti ¼ Eit ; Sit Gti t ¼ time; where Eit is the set of entities that compose the environment inhabited by entity i, at time t; Sit the set of attributes that represent entity i, at time t; Gti the expression that defines the main goal of entity i, at time t.
t S t ¼ Sft ,Sm :
Another assumption is that entities are capable of devising plans: Pt ¼ pðE t ; St ; Gt Þ
or E t ; St ; Gt -pt ;
where a plan is a set of action descriptors pt ¼ at1 at2 ; y :
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The above assumptions, as well as the goal fulfillment situation, can all be described in some formal language as powerful as the mathematical first order logic. This formal description is not one of the main concerns of this paper, since theorems are not to be derived. However, for the purpose of clarity, the simplified assumptions and the situation of goal achievement, adopted in this work for the goal-oriented dead reckoning, are expressed in first order logic. In this way, the plan is a sequential list of actions, p ¼ ½a1 ; a2 ; y and the main goal Gt is a sentence in the normal disjunctive form, 8x1 ; 8x2 ; yGt ¼ b1 3b2 3y;
p=[move(MrBlue, 2.0, 0, 0), move(MrBlue, 2.0, 0, 3.0)] An example of a conditional action is: if (facing(MrBlue, danger), deviateRight(MrBlue), continue) That is, if Mr. Blue faces any danger, he deviates right, otherwise he continues. The formalization of a planner is not in the scope of this paper and its implications are discussed elsewhere [18]. The goal-oriented dead reckoning is presented in this paper in the form of a pseudo-language algorithm, which is listed in the Appendix. For a quicker understanding, Fig. 3 shows a graphical representation that summarizes the proposed generalization as follows: *
where xn is a variable and bk is a conjunction of literals, bk Lk1 4Lk2 4y : Literals, on the other hand, are predicates, negated or not, that may include constants, function symbols and variables. In this simplified case, the goal is achieved by an entity i, at time t0 , when the following relation holds:
*
0
( k such that fLk1 ; Lk2 ; ygDSit : Examples of a simple goal (without variables) and a simple plan are: G=(positionX(MrBlue, 2.0) 4 positionZ(MrBlue, 3.0)) 3 happy(MrBlue)
*
At time t0, the pilot sends its initial attributes and the main goal ðSi0 ; G0i Þ to clones. The pilot devises and executes its plan. Upon receiving the pilot’s messages, clones devise and execute prediction plans ðEj0 ; Sj0 ; G0i -p0j Þ: At some time t1, the pilot sends a recovery goal ðg1i Þ to clones. Upon receiving the recovery message, clones verify if inconsistency is greater then the acceptable limit ðd > dlim Þ: If a given clone finds such condition true, it suspends the prediction plan, devises a recovery plan (E1j , S1j , g1i -p1j .) and starts executing it. Otherwise, the clone continues without re-planning. At time t2, the recovering clone reaches the intermediate goal and takes on again the main goal,
Fig. 3. Graphical representation of goal-oriented dead reckoning.
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*
devising and executing another prediction plan ðEj2 ; Sj2 ; G2j -p2j Þ: At time t3, the pilot achieves the main goal and communicates the fact to clones. Upon receiving this information, clones start time-out counters. If a clone does not reach the main goal before time-out, then it will probably have its attributes updated to satisfy the given goal.
Traditional dead reckoning as well as joint angle and physical action dead reckoning are special cases of goaloriented dead reckoning. In other words, previous versions can be described in terms of prediction and recovery mechanisms, considering purely physical goals, of course. The traditional form is normally applied to vehicles that are guided by joysticks or similar devices. In this case, the final state cannot be predetermined and the main goal is actually unknown. Therefore, the main goal and achieved goal messages are never sent to clones. The prediction plan is reduced to the second order polynomial approximation, or some equivalent algorithm, that depends only on the entity’s present attributes. ðSjt -ptj Þ: Update messages are special cases of recovery messages where the transmitted recovery goals are purely physical states. The convergence algorithm, on the other hand, is a particular type of recovery plan that does not depend on the environment’s conditions ðSjt ; gtj -ptj Þ: In traditional dead reckoning, clones converge to the pilot’s state every time an update message is received. This is equivalent to setting the inconsistency limit to zero (dlim=0) in the goal-oriented dead reckoning algorithm. The same reasoning holds true for joint angle dead reckoning, which is traditional dead reckoning applied to the several parts of an articulated entity. In physical action dead reckoning, the main goal is given in the form of the action to execute in order to
achieve it. Clones never recover. This is, of course, a more restrictive form of goal-oriented dead reckoning where clones cannot choose the sequence of actions to perform and where recovery is not considered. The properties of the various types of dead reckoning are summarized in Table 1. The qualitative values (high, medium, low) presented in the table are not a result of theoretical calculations or empirical measurements; they are only given as indications of the potentialities of each type of dead reckoning. As a generalization, the goal-oriented dead reckoning allows the degree of autonomy to be adapted to virtual component type and to environment needs. In fact, even clones of non-autonomous characters can be given some autonomy to avoid overlapping.
9. Experimental results In order to implement prototypes and obtain experimental results, a framework for networked autonomous characters is being developed. The framework is built on top of Bamboo [19,20], an open-architecture language independent toolkit for developing dynamically extensible, real-time, networked applications. Bamboo’s design focuses on the ability of the system to configure itself dynamically, which allows applications to take on new functionality after execution. Bamboo is particularly useful in the development of scalable virtual environments on the Internet because it facilitates the discovery of virtual components on the network at runtime. The framework for distributed autonomous characters offers a set of classes that provides the basic functionality needed for shared state management using
Table 1 Comparison of the various dead reckoning types Traditional dead reckoning Main goal Recovery goal Prediction plan Recovery plan Minimum recovery rate Consistency Maximum visual soundness Maximum clone autonomy
Joint-angle dead reckoning
Physical action dead reckoning
Goal-oriented dead reckoning
Physical (unknown) Physical Polynomial Convergence algorithm Medium (based on state error) Physical (high) Medium
Physical Physical Kalman filtering State update
Physical F Action scripts F
Physical/mental Physical/mental Devised by clone Devised by clone
High (based on state error) Physical (high) Medium
F Physical (medium to high) Medium
Low (based on environment) Goal (low to high) High
None
None
Medium
High
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goal-oriented dead reckoning. It also supports frequent state regeneration and previous versions of dead reckoning as special cases. Several examples were run in a Windows NT environment producing good results, although performance was not formally investigated. Two of these examples are shown in Figs. 1 and 2. Fig. 4 illustrates the case where two charactersFMr. and Mrs. BlueFare ordered to achieve the ‘‘very happy’’ state, a purely mental goal. Mr. and Mrs. Blue become happier as they approximate each other. When they get close, they both demonstrate to be very happy by raising their arms. Since the goal does not specify physical attributes, the final positions of Mr. and Mrs. Blue are different at each host.
10. Conclusions and future work Autonomy of virtual humans or A-Life entities is not a well-explored concept in the area of networked virtual environments. On the other hand, most of the people working on autonomous characters are not focused on shared state management over a computer network. This paper explores a common view amongst these areas of research and proposes innovative concepts for shared state management. Firstly, state consistency is defined in terms of physical and mental states. Secondly, visual soundnessFa concept ‘‘inversely proportional’’ to state consistencyFis associated with the autonomy of clones (regardless of the pilot’s natureFa user or an autonomous creature). Finally, a new extension to the concept of dead reckoning is proposed, which is particularly advantageous for autonomous characters. The advantages and worries of this new approach, called goal-oriented dead reckoning, are presented below. Although one has already mentioned that state prediction may be object-specific and dead reckoning should handle any type of shared state [1], these two ideas are not clearly explained in the literature. For instance, the authors of Improv [11] do not present this system in terms of shared state management, although its scripted events have been recognized as a form of object-specific state prediction elsewhere [1]. JackMOO [9] and VLNET [10] are also impressive works on distributed virtual actors but in these systems, like in Improv, clones have very limited autonomy. As far as collision is concerned, real-time distributed collision agreement for dead reckoning algorithms are not clearly mentioned in the literature [1]; and the existent systems probably fail the test of visual soundness. Most of the interesting dead reckoning extensions, such as the position history-based protocol [21], produce smooth animation but cannot cope with undesirable collisions that happen when the predicted trajectory differs from
the real one. One possible solution for many of the above-mentioned drawbacks is to relax the concept of dead reckoning by exploring the idea of autonomy. Therefore, this paper proposes the goal-oriented dead reckoning, which allows clones to be empowered by autonomy in order to produce visually sound networked virtual environments. The proposed shared state management mechanism also works in favor of scalability in the sense that it tends to substantially reduce the number of transmitted network messages. Moreover, it is expected that the contributions of the goal-oriented dead reckoning, with respect to scalability, become more significant as mental models are improved, since clones will tend to need less instructions from the pilot. It should be mentioned that, like physical action dead reckoning, the goal-oriented dead reckoning requires that special care be given to the interaction between autonomous characters and moving objects. If, for example, while ‘‘Mr. Green’’ is dancing, a ball is thrown, it might hit ‘‘him’’ at one host and not hit ‘‘him’’ at another host. Either the ball’s trajectory is allowed to be different at each host, which will most likely make pilot and clones end up at different positions, or the ball is forced to always follow its pilot’s path, which will probably produce unrealistic scenes. As compared with previous versions, which are just special cases, the generalized prediction and recovery mechanisms are, of course, harder to program. A greater share of each host’s computational capacity is also needed. Clones that possess autonomyFas in physical action or goal-oriented dead reckoningFrequire a more elaborate initialization. When a new participant joins the environment, existing actors may be executing an action or fulfilling a goal. Therefore, initialization of clones at the new user’s host cannot be done with a simple state update. If, to increase scalability, the environment is divided in areas of interest associated to multicast groups, then clones must be created and initialized as soon as the pilot crosses region boundaries. There are several topics for future work. For instance, performance of the goal-oriented dead reckoning and its impact on scalability should be formally investigated. Furthermore, a complexity analysis of the proposed algorithm is required. Recovering criteria should also be studied in more detailsFon the pilot’s side, this concerns the set of rules used to dynamically adapt the recovery message rate; on the clones’ side, the focus is on the set of rules used to trigger recovery. Complex conditional actions and complex plans including parallel actions should also be considered. Moreover, resource management techniques for scalability need to be carefully examined. Real-time distributed collision agreement is another important issue for further research.
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Fig. 4. Mr. and Mrs. Blue go after the ‘‘very happy’’ state.
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Acknowledgements The authors would like to thank the CNPq for the financial support. Appendix .oal oriented dead reckonin. { vector/planS plans of pilot[MAX PILOTS]; vector/planS plans of clone[MAX CLONES]; vector/goalS main goals of clone[MAX CLONES]; inconsistency d[MAX CLONES]; const inconsistency dlim[MAX CLONES]; /* prediction and recovery mechanisms while (true) for (each pilot Pi) /*prediction: send main goals and achieved goals if (new goal Gti ) send reliable message with goal Gti to clones; Eti ; Sti ; Gti -pti ; insert pti in plans of pilot[i]; endif for (each pk in plans of pilot[i]) execute a plan step; if (Sti satisfies pk’s goal) send reliably to clones ‘‘Gk achieved’’; erase pk; endif endfor /*recovery: send recovery messages if ((plans of pilot[i] is not empty) and (recovery message is needed)) send recovery goal gti to clones; endif endfor for (each clone Cj) if (new message) /*prediction: treat main goal if (message carries main goal G) if (no main goal is on timeout count) insert G in main goals of clone[j]; if (recovery plan is not running) Etj ; Stj ; G-ptj ; put ptj in plans of clone[j]; endif indicates that message has been treated; endif /*prediction: treat achieved goal else if (message is of type ‘‘G achieved’’) if (G is in main goals of clone[j]) start timeout counter for G; indicates that message has been treated;
/*recovery: treat recovery messages else if (message carries recovery goal g) if ðd½j > dlim ½jÞ empty plans of clone[j] Etj ; Stj ; g-ptj ; mark ptj as recovery plan; insert ptj in plans of clone[j]; else if (recovery plan is running) erase recovery plan; for (each Gk in main goals of clone[j]) Etj ; Stj ; Gk -ptj ; insert ptj in plans of clone[j]; endfor endif indicates that message has been treated; endif endif /*prediction and recovery: plan execution for (each pk in plans of clone[j]) execute a plan step; if (Stj satisfies pk’s goal) if (pk is the recovery plan) for (each Gn in main goals of clone[j]) Etj ; Stj ; Gn -ptj ; insert ptj in plans of clone[j]; endfor else erase pk’s goal from main goals of clone[j]; endif erase pk; endif endfor /*prediction: goal timeout for (each Gk in main goals of clone[j]) if (timeout) erase pk from plans of clone[j]; force Stj to satisfy Gk; erase Gk; endif endfor endfor endwhile }
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