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A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems
CHAPTE R 2
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems Manoel T. de Abreu Netto, Baldoino F. dos Santos Neto, Carlos J.P. de Lucena Department of Computer Science, PUC-Rio, Rio de Janeiro, RJ, Brazil
2.1 Introduction The approach of self-organizing systems has increased its relevance and is used to deal with complex domains. The use of this approach enables the development of decentralized systems that exhibit certain dynamicity and adaptability to couple with previously unknown perturbations (Serugendo et al., 2003, 2005). According to principles of self-organization, each component of the autonomic system obtains and maintains only local information available in the environment, in a decentralized way and without any external control, being restricted only to local interactions. It is on the basis of these interactions that the system exhibits it macroscopic behavior, which may be observed from a global point of view (Huebscher and McCann, 2008). The multiagent system paradigm has been considered a promising solution for the building of self-organized systems (Sycara, 1998; Jennings and Wooldridge, 2000). Multiagent systems are a group of agents that cooperate to autonomously achieve a required systemwide or macroscopic behavior by using only local interactions, local activities of the agents, and locally obtained information. Although the multiagent system paradigm is a feasible solution for the construction of selforganized systems, two of the main challenges faced today are: (1) the lack of mechanisms to regulate the interaction and coordination among the agents in the environment and (2) the infrastructure necessary to implement such mechanisms. Several works, (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Babaoglu et al., 2006; De Wolf, 2007) have provided guidelines for expressing a series of recurrent design patterns present in self-organizing systems as a first step to face the lack of mechanisms to regulate agent interaction and coordination. Self-organizing patterns provide the directions and motivate the reuse of solutions already adopted in the process of constructing such systems. However, there is currently no infrastructure to support the implementation of the self-organizing multiagent system based on the proposed patterns. In this chapter we propose JASOF – Jadex Self-Organization Framework, a pattern and a BDI-based framework that extends Jadex (Poukahr and Braubach, 2007) by providing a set Advances in Artificial Transportation Systems and Simulation. Copyright © 2015 Zhejiang University Press Co., Ltd. Published by Elsevier Inc. All rights reserved.
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22 Chapter 2 of capabilities, composed of plans and goals that implement the basic patterns described in (Gardelli et al., 2007). The primary goal of JASOF is to contribute to the self-organization area through software engineering techniques. This is accomplished by providing a framework that allows for implementation mechanisms to regulate the interaction and coordination between agents of a self-organized system through the reuse of previously proposed solutions (Sudeikat and Renz, 2008; Sudeikat et al., 2009; Fayad and Johnson, 1999; Neto et al., 2009a; Neto et al., 2009b). The chapter is organized as follows. In Section 1.2, JASOF is detailed and Section 1.3 reports a case study while describing how to extend the framework to implement a solution to the automatic guided vehicle problem. Section 1.4 presents some related work. Finally, Section 1.5 concludes this study and presents some future work.
2.2 JASOF In this section, we describe the main idea of the proposed framework, followed by a description of how to implement the environment and patterns available to regulate and coordinate the agents that act in such an environment.
2.2.1 Main Idea Due to the complexity of building self-organizing systems, (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Dobson et al., 2006) have elaborated a series of recurrent design patterns that are commonly present in these systems. The aforementioned patterns aim to smooth the process of constructing self-organizing systems by offering a set of directions and making adequate reuse of solutions that have already been tested and documented. In this context, the JASOF framework, which is based on the BDI (Weiss, 1999) paradigm extending Jadex, provides the necessary infrastructure to implement the patterns and the environment where the agents operate. In Jadex, from the external point of view, the agent is a black box that receives and sends messages. Messages, internal events, and new goals are used as input for the reaction and deliberation mechanism (Reaction Deliberation). Then, based on these results of deliberation, the mechanism directs the events to the plans that are already running or those that can be called up from the library plans. The plans, when running, can modify the knowledge base of the agents, send messages to other agents, create new subtargets, and enable internal events. The reaction mechanism and resolution is generally the same for all agents. However, the behavior of a specific agent is determined by its beliefs (Beliefs), goals (Desires) and plans (Intentions). The patterns in JASOF are provided as a set of goals and plans encapsulated as Jadex capabilities, which can be easily imported and implemented by an agent. The environment is modeled as a space composed of interconnected positions following the notion of a grid. Furthermore, it is based on the agents-and-artifacts (A&A) approach (Gardelli et al., 2008), which considers the environment populated by agents and artifacts,
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Figure 2.1: JASOF Class Diagram (Environment)
where artifacts are reactive agents providing resources and services, meaning an active environment. The class diagram in Figure 2.1 shows the structure of the environment, where the class Environment contains information about the available locations, the agents in the system and your neighborhood. These locations are represented by the class Location that provides information about its artifacts (class Artifact), their manager agent and their relative position in the environment (class Position).
2.2.2 JASOF Structure First, we show how the environment is modeled and how it acts in our framework through the Agent Location. Then, the plans provided by JASOF that enable the implementation of the referred patterns are described in detail. Figure 2.2 shows the class diagram that represents these plans and the hotspots from which it is possible to create new patterns not yet documented in the literature. In Figure 2.3 we have the basic JASOF architecture, showing the components of JASOF, described below, and the interconnection between the application and Jadex. 2.2.2.1 Environment In natural self-organizing systems and artificial prototypes, the environment plays a key role in the system dynamics. It is through the interaction with the environment that self-organization of different systems is achieved. A typical example is that of ants foraging, which perform interactions with the environment such as storage of pheromones and their perception, which then guide their decisions and do their work. Therefore, the environmental model is a key
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Figure 2.2: JASOF Class Diagram (Plans)
part of the framework to build such systems. It is from the environment that the patterns of self-organization are possible. Thus, it is available in the JASOF modeled as a set of discrete locations, interconnected to form a grid in two dimensions and, in addition, as mentioned above, is based on the A&A approach, giving it an active environmental characteristic. At
Figure 2.3: JASOF Architecture
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems 25 each position of the environment a location is allocated. It is important to define the difference between them: position refers to a reference point in the environmental space, whereas location is an area located in a position that can be inhabited by multiple agents. In each location there is an agent responsible for its management; this agent is named Agent Location. 2.2.2.2 Agent location As already said, each location in the environment has an agent, called Agent Location, responsible for managing the artifacts stored in this location, to execute the patterns and to provide artifact access to agents that are in the same position. It is through the agent Location that the environment has an active behavior, executing and interacting with the other participants of the system. Those participants are comprised of all agents in the environment, Locations and others. The agents that are not Location are called User agents in the framework. Through communication with the Agent Location of a specific location it is possible for a given agent to insert an artifact, which can represent relevant information such as a pheromone, or to read the stored artifact. All communication with the Agent Location to insert and read is a frozen spot of the framework. The reason for associating agents to each location is justified by the need to search for forms of decentralization, despite the large number of agents and the communication required for reading and writing information. In the following sections the ease the environment and agents have in implementing self-organization patterns can be observed. 2.2.2.3 Diffusion pattern The idea behind the Diffusion Pattern is the process present in nature that when a pheromone is deposited into the environment, it spontaneously tends to diffuse to neighboring locations (Weyns et al., 2008). Diffusion distributes the information equally across all neighboring locations. In order to implement the diffusion pattern it is necessary for the agent to import the DiffusionPattern capability, depicted in Figure 2.4. It provides the infrastructure needed for an agent to send information, decreased by a factor, to a set of neighbors selected according to a rule (PropagationRule) and perform the actions related to the coordination process or react to a received message. This is, in essence, the coordination mechanism. Such capability has the class DiffusionPatternPlan, Figure 2.2, which needs to be extended in order to define the rules for selection of agents to receive the message (method DestinationRule), the actions of a coordination (method CoordinationRule) and the propagation (method PropagationRule) process. As we can see in Figure 2.4, the message diffusionMsg is the standard event used to perform the diffusion pattern, so importing that capability implies that the agent can receive and handle that message. If necessary it is possible to modify the ReceiveRule method and change this process.
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Figure 2.4: Diffusion Capability
2.2.2.4 Evaporation pattern Evaporation can be considered a mechanism to reduce the amount of information, based on a time relevance criterion, avoiding an overload of information. The Evaporation Pattern can be implemented by an agent, using JASOF, by importing the EvaporationPattern capability. Figure 2.5 shows that such capability will periodically execute
Figure 2.5: Evaporation Capability
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems 27 the evaporate() method, present in the class Artifact, of the instances contained in the Belief Base of the respective agent Location. To change the frequency it is necessary to modify the parameter recurdelay that by default is 6000, as shown in Figure 2.5. Another parameter that can be determined is how much of the information will be evaporated per cycle; in this case the attribute factor of the Artifact class must be changed. 2.2.2.5 Aggregation pattern Aggregation is a mechanism of reinforcement and is also observable in human social tasks (Gardelli et al., 2007). When redundant information is stored, its relevance increases. As in the other patterns, to implement the Aggregation patterns it is necessary to import the EvaporationPattern capability, which captures the concept of Evaporation, periodically checks the artifacts saved in the Belief Base and checks which of them may suffer an aggregation. This aggregation is accomplished by the method aggregate() of the Artifact class. An aggregation process between two artifacts produces one with more relevance. 2.2.2.6 Replication pattern Natural systems typically feature replication mechanisms in order to increase security and robustness. The Replication pattern works in a way similar to the Diffusion pattern; the main difference resides in the factor of the information replicated. In the diffusion case, the artifact suffers a decrease in the factor attribute and, on the other hand, the replication does not modify that attribute, keeping its actual value. The mechanisms DestinatioRule, CoordinationRule, and PropagationRule are used for the same purpose.
2.2.3 JASOF Hotspots The framework hotspots are: 1. The ability to specify the information to be used in the communication process: Using the class Artifact it is possible to create new types of data to be stored and exchanged by agents. 2. Pattern composition for the creation of new self-organizing mechanisms: Through Jadex features it is possible to include several plans in an agent, creating the potential for patterns composition. 3. The creation of new basic patterns: Flexibility through the extension of the PatternPlan class and the implementation of its respective plan. 4. Coordination and propagation mechanisms to be specified in each pattern: It is possible to build a self-organizing solution on the basis of ant foraging or in a Gossip Protocol, just modifying this mechanism.
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2.3 Case Study: A Self-Organized Automatic Guided Vehicle Automated guided vehicles (AGVs) are fully automated, custom-made vehicles that are able to transport incoming loads (i.e., packets, materials, and/or products) in a logistical or production factory environment (Weyns and Holvoet, 2008; Weyns et al., 2008). AGV systems can be used for distributing manufactured products to storage locations or as an inter-process system between various production machines. An AGV system receives transport requests for loads from a factory management system or machine operating software, and instructs AGVs to execute transports. We have used the JASOF framework to build a self-organizing multiagent system in order to implement a decentralized solution to the AGV problem. Furthermore, the system can withstand perturbations, such as unavailable routes or barriers in the path.
2.3.1 Main Idea In order to implement a solution to this problem we have modeled the AGV system using the JASOF framework with four agents: (1) Destination Agent, representing the destination; (2) Warehouse Agent, representing the warehouse; (3) Transporter Agent, representing the AGV; (4) Location Agent, representing the Stations between the warehouses and the destinations. As we can see in Figure 2.6, which illustrates a reduced situation, the architecture is modeled as follows: the locations where packages must be picked up are located on the left (Warehouse), the destinations on the right (Destination), while the network in between consists of Locations and connecting segments on which AGVs are located (AGV Location). Each individual AGV (Transporter) is capable of only a limited set of local Actions, such as Move, Pick Load and Drop Load. The vehicles move loads in a factory by moving towards the warehouse location: picking up the load, moving towards the destination location, and dropping the load. The fleet of AGVs has to self-organize efficiently to accommodate a current request for transport and to withstand unpredictable situations. In the next subsection we present more details about such agents.
Figure 2.6: AGV Environment Structure
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2.3.2 Destination Agent In our solution using the framework, this agent is responsible for receiving the packages transported by the Transporter agent and to inform, using the diffusion pattern, the neighborhood of what it is capable of doing. This information is communicated to the Location agents that are interconnected by one segment, excluding the other types of agents. This agent was implemented as a Jadex agent importing the DiffusionPattern capability of the framework. This pattern has a plan with four mechanisms of extensibility: receive, destination, coordination, and propagation. Receive and destination mechanisms were not modified though. The standard solution was adopted. However, the DestinationRule was extended, regulating that only agents Location were able to receive the diffusionMsg. Furthermore, the PropagationRule was extended to determine the type of information to be diffused. Complementing the functionality of the Destination agent, a new plan was created to define the protocol specifying the messages used to receive new packages from the Transporter agent.
2.3.3 Warehouse Agent This agent is responsible for managing the presence of packages in its location. When a new package arrives, the agent runs the diffusion pattern to propagate the information in the environment, advising that a new load is available and ready to be transported. As the Destination agent, the mechanisms ReceiveRule and CoordinationRule have been maintained as the default option and the DestinationRule and PropragationRule were modified for the same purpose. The information diffused contains the position of the Destination agent in the environment, used by the agent Transporter to build its route.
2.3.4 Transporter Agent This agent represents the AGV, responsible for transporting packages from warehouses to their destinations. Their actions are: Move, Pick Load and Drop Load. Each action was modeled as a step in a recurrent goal: while the Transporter is not loaded with a package to be transported, the agent will move (action Move) past its neighbors for information about locations that may eventually have a package to be transported; this movement is performed until the agent finds a package. In our solution the battery was not considered a problem. Once a package is found, the Transporter agent carries the load (Pick Load) and then the plan to deliver the package is triggered. The agent will travel through the environment looking for the destination and will build a route collecting the information available in the locations. When the agent finds the destination, a message is sent to the Destination agent at that position to dispatch the package. Finally, the process is restarted.
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2.3.5 Location Agent On this occasion, the Location agent performs the Evaporation, Aggregation, and Diffusion patterns. The Evaporation pattern, as seen in the Section 1.2.2.4, occurs periodically performing the decrement of the relevance factor from the information stored in the location. It is the Aggregation. However, in this case, aggregation is performed between similar information stored. The Diffusion pattern occurs when the diffusionMsg received from other agents has the parameter path higher than 0, indicating that the information has to be diffused again.
2.3.6 Execution The execution can be described through the six events that occur. They are: 1. Initializing the environment; the locations are put in their positions and their respective Location agents are instantiated as well as the agents Warehouse and Destination. As we can see in Figure 2.7. 2. As soon as the Destination agent is created, the diffusion process starts by sending information to the neighborhood about its availability and position. As we can see in Figure 2.8. 3. Adding a package – when a package is loaded in a Warehouse, the responsible agent starts to diffuse to the neighborhood that a new package is waiting to be carried out. As shown in Figure 2.9.
Figure 2.7: Initializing the Environment
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Figure 2.8: Diffusion Process
4. While these events occur, the Location agents have already started the evaporation process. The ones near the agent Destination and Warehouse evaporate the recent information about position and package, respectively. 5. The Transporter agent is looking for information about available packages to be picked up. It is through the agents Locations that it builds its route, as we can see in Figure 2.10. 6. When the Transporter agent finds a package, it starts to look for information about destinations, similar to the step described above. When the destination is found the Transporter agent reinitializes the search for another package.
Figure 2.9: Warehouse diffusing a package availability
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Figure 2.10: Dispatch Process
2.3.7 System Composition A relevant motivation for the separation of the mechanisms of self-organization in simple patterns is the resulting possibility of forming more sophisticated and undiscovered ones, from just a simple composition of patterns. This separation is one of the main challenges in engineering self-organizing systems, as discussed in (Parunak and Brueckner, 2011). As noted, the JASOF framework allows the construction of agents that perform specific mechanisms of self-organization and their combination. Also, as the case study illustrates, we can have agents with different mechanisms interacting in the system, and the result of this composition can be the emergence of known high-level mechanisms, like stigmergy or gradient fields, or even others not yet described in the literature. The AGV case study was modeled on the basis of the construction of a system supported by indirect communication among the Transporters through the environment, using the notion of pheromones. We used the standard Evaporation, Diffusion, and Aggregation patterns in Location entities. However, our solution to the problem in finding AGV routes could be based on the gradient fields idea, where from these same patterns we could compose a new solution, just changing the hotspots points: mainly the propagation method and the Artifact class. Another possible self-organizing solution for the AGV problem, using the JASOF framework, could be through the Gossip protocol that has been shown to be robust in the presence of failures of individual nodes (Gavidia et al., 2006). As an example, it has been used in wireless mesh networks to propagate a news device, or to disseminate state information with the purpose of building hierarchies of clusters in wireless sensor networks (Iwanicki and van Steen, 2009).
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2.4 Related Work In (De Wolf and Holvoet, 2006), two of the main decentralized coordination mechanisms are described in a structured manner. They are the gradient field and market-based control. These mechanisms are dealt with as patterns to be adopted when searching for a decentralized organization solution. Furthermore, (De Wolf, 2007) describes how these patterns need to be adopted and the consequences of their adoption. Nevertheless, the pattern descriptions are only described at the conceptual level, only allowing the formulation of high-level solutions. The authors are not concerned about the implementation aspects, or in supporting the software development, that is, how to structure the self-organization system using agents and classes. Following the same approach as De Wolf (De Wolf and Holvoet, 2006), (Gardelli et al., 2007) describes the above mentioned patterns in terms of low-level patterns, which can be called basic patterns. They are: Collective Sort, Diffusion, Aggregation, Replication, and Evaporation. However, the approach used is the same and there is still no concern with the implementation of these patterns.
2.5 Conclusions and Future Work In this chapter we presented JASOF, a pattern-based framework that provides built-in selforganizing patterns implemented following the BDI architecture. Self-organizing patterns have only been described so far (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Babaoglu et al., 2006) as a very high level of abstraction without any indication of how they could be implemented. In our proposed solution, self-organizing patterns modularized into capabilities are the basic building block to construct self-organizing multiagent systems or new self- organizing patterns. The JASOF approach was illustrated using the AGV case study. The AGV encompasses three different agents: (1) Location –- implements the evaporation, diffusions, and aggregation; (2) Warehouse – implements the Diffusion pattern; and (3) Destination – implements the Diffusion pattern. This case study shows the viability of JASOF for the implementation of complex and decentralized problems, in addition to illustrating how such problems can have a facilitated solution by the composition of self-organizing patterns and simply implemented using the BDI architecture or, more specifically, using agents goals and plans modularized into Jadex capabilities. This framework is a first step towards a complete infrastructure where complex and decentralized systems can be automatically generated from high-level specifications, such as domainspecific languages. As a future work, we intend to validate our ideas in more complex case studies and suggest some guidelines to help the creation of new self-organizing patterns using our proposed infrastructure.
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Acknowledgment This work is supported by the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ.
References Babaoglu, O., Canright, G., Deutsch, A., Di Caro, G.A., Ducatelle, F., Gambardella, L.M., Ganguly, N., Jelasity, M., Montemanni, R., Montresor, A., Urnes, T., September 2006. Design patterns from biology for distributed computing. TAAS 1 (1), 26–66. De Wolf, T., 2007. Analysing and engineering self-organising emergent applications. Ph.D. Thesis. Department of Computer Science, K.U.Leuven, Leuven, Belgium. May, 2007. De Wolf, T., Holvoet, T., 2006. Decentralised coordination mechanisms as design patterns for self-organising emergent applications, Proceedings of the Fourth International Workshop on Engineering Self-Organising Applications. In: Brueckner, S., Hassas, S., Jelasity, M., Yamins, M. (Eds). Future University-Hakodate, Japan, pp. 40–61. Dobson, S., Denazis, S., Fernández, A., Gaiti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., Zambonelli, F., December 2006. A survey of autonomic communications. TAAS 1 (2), 223–259. Fayad, M., Johnson, R., 1999. Building Application Frameworks: Object-Oriented Foundations of Framework Design (Hardcover), first ed. Wiley, ISBN-10: 0471248754. Gardelli, L., Viroli, M., Casadei, M., Omicini, A., 2008. Designing self-organising environments with agents and artefacts: a simulation-driven approach. IJAOSE 2 (2), 171–195. Gardelli, L., Viroli, M., Omicini, A., 2007. Design patterns for self-organizing multi-agent systems. In: Wolf, T.D., Sare, F., Anthony, R., (Eds), 2nd International Workshop on Engineering Emergence in Decentralised Autonomic System (EEDAS) 2007. ICAC 2007, Jacksonville, Florida, USA. June 2007. CMS Press, University of Greenwich, London, UK. pp. 62–71 Gavidia, D., Voulgaris, S., van Steen, M., 2006. A gossip-based distributed news service for wireless mesh networks. In: Proceedings. Third IEEE Conference on Wireless On demand Network Systems and Services (WONS). Les Menuires, France. Huebscher, M.C., McCann, J.A., 2008. A survey of Autonomic Computing – Degrees, Models, and Applications. ACM Computing Survey. Iwanicki, K., van Steen, M., 2009. Multi-hop cluster hierarchy maintenance in wireless sensor networks: a case for gossip-based protocols. In: Proceedings Sixth European Conference on Wireless Sensor Networks (EWSN). Cork, Ireland. Jennings, N.R., Wooldridge, M., 2000. Agent-oriented software engineering. In: Bradshaw, J. (Ed.), Handbook of Agent Technology. AAAI/MIT Press. Neto, B., Costa, A., Netto, M. T. A., Silva, V., Lucena, C., 2009a. JAAF: A Framework to Implement Self-Adaptive Agents. Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering. (SEKE’2009). Neto, B., Costa, A., Silva, V., Lucena, C., Netto, M. T. A., 2009b. JAAF-S: A Framework to Implement Autonomic Agents Able to Deal with Web Services. Proceedings of the 4th International Conference Proceedings of the 4th International Conference on Software and Data Technologies (ICSOFT’09). Parunak, H.V.D., Brueckner, S.A., May, 2011. Software engineering for self-organizing systems. In: Proceedings of Twelfth International Workshop on Agent-Oriented Software Engineering (AOSE 2011). Taipei, Taiwan. Poukahr, A., Braubach, L., 2007. Jadex User Guide, Distributed System Group University of Hamburg, Germany, Release 0.96.
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems 35 Serugendo, G., Di, M., Gleizes, M.-P., Karageorgos, A., 2005. Self-Organisation in MAS. Knowledge Engineering Review 20 (2), 165–189, Cambridge University Press. Sudeikat, J., Braubach, L., Pokahr, A., Renz, W., Lamersdorf, W., 2009. Systematically engineering self-organizing systems: The SodekoVS Approach. Proceedings des Workshops über Selbstorganisierende, adaptive, kontextsensitive verteilte Systeme (KIVS 2009). Sudeikat, J., Renz, W., 2008. On the encapsulation and reuse of decentralized coordination mechanisms: A layered architecture and design implications. In: Communications of SIWN, vol. 4, no. ISSN 1757–4439, pp. 140–146. Sycara, K., 1998. Multi-agent Systems. Artificial Intelligence 10 (2), 79–93. Weiss, G., 1999. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, MA, USA. Weyns, D., Holvoet, T., Jan. 2008. 2008. Architectural design of a situated multi-agent system for controlling automatic guided vehicles. Int. J. Agent-Oriented Softw. Eng 2 (1), 90–128 http://dx.doi.org/10.1504/ IJAOSE.2008.016801. Weyns, D., Holvoet, T., Schelfthout, K., Wielemans, J., 2008. Decentralized control of automatic guided vehicles: applying multi-agent systems in practice. In: Companion To the 23rd ACM SIGPLAN Conference on ObjectOriented Programming Systems Languages and Applications (Nashville, TN, USA, October 19–23, 2008). OOPSLA Companion ’08. ACM, New York, NY, 663–674. doi:10.1145/1449814.1449819.
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems Manoel T. de Abreu Netto, Baldoino F. dos Santos Neto, Carlos J.P. de Lucena Department of Computer Science, PUC-Rio, Rio de Janeiro, RJ, Brazil
2.1 Introduction The approach of self-organizing systems has increased its relevance and is used to deal with complex domains. The use of this approach enables the development of decentralized systems that exhibit certain dynamicity and adaptability to couple with previously unknown perturbations (Serugendo et al., 2003, 2005). According to principles of self-organization, each component of the autonomic system obtains and maintains only local information available in the environment, in a decentralized way and without any external control, being restricted only to local interactions. It is on the basis of these interactions that the system exhibits it macroscopic behavior, which may be observed from a global point of view (Huebscher and McCann, 2008). The multiagent system paradigm has been considered a promising solution for the building of self-organized systems (Sycara, 1998; Jennings and Wooldridge, 2000). Multiagent systems are a group of agents that cooperate to autonomously achieve a required systemwide or macroscopic behavior by using only local interactions, local activities of the agents, and locally obtained information. Although the multiagent system paradigm is a feasible solution for the construction of selforganized systems, two of the main challenges faced today are: (1) the lack of mechanisms to regulate the interaction and coordination among the agents in the environment and (2) the infrastructure necessary to implement such mechanisms. Several works, (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Babaoglu et al., 2006; De Wolf, 2007) have provided guidelines for expressing a series of recurrent design patterns present in self-organizing systems as a first step to face the lack of mechanisms to regulate agent interaction and coordination. Self-organizing patterns provide the directions and motivate the reuse of solutions already adopted in the process of constructing such systems. However, there is currently no infrastructure to support the implementation of the self-organizing multiagent system based on the proposed patterns. In this chapter we propose JASOF – Jadex Self-Organization Framework, a pattern and a BDI-based framework that extends Jadex (Poukahr and Braubach, 2007) by providing a set Advances in Artificial Transportation Systems and Simulation. Copyright © 2015 Zhejiang University Press Co., Ltd. Published by Elsevier Inc. All rights reserved.
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22 Chapter 2 of capabilities, composed of plans and goals that implement the basic patterns described in (Gardelli et al., 2007). The primary goal of JASOF is to contribute to the self-organization area through software engineering techniques. This is accomplished by providing a framework that allows for implementation mechanisms to regulate the interaction and coordination between agents of a self-organized system through the reuse of previously proposed solutions (Sudeikat and Renz, 2008; Sudeikat et al., 2009; Fayad and Johnson, 1999; Neto et al., 2009a; Neto et al., 2009b). The chapter is organized as follows. In Section 1.2, JASOF is detailed and Section 1.3 reports a case study while describing how to extend the framework to implement a solution to the automatic guided vehicle problem. Section 1.4 presents some related work. Finally, Section 1.5 concludes this study and presents some future work.
2.2 JASOF In this section, we describe the main idea of the proposed framework, followed by a description of how to implement the environment and patterns available to regulate and coordinate the agents that act in such an environment.
2.2.1 Main Idea Due to the complexity of building self-organizing systems, (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Dobson et al., 2006) have elaborated a series of recurrent design patterns that are commonly present in these systems. The aforementioned patterns aim to smooth the process of constructing self-organizing systems by offering a set of directions and making adequate reuse of solutions that have already been tested and documented. In this context, the JASOF framework, which is based on the BDI (Weiss, 1999) paradigm extending Jadex, provides the necessary infrastructure to implement the patterns and the environment where the agents operate. In Jadex, from the external point of view, the agent is a black box that receives and sends messages. Messages, internal events, and new goals are used as input for the reaction and deliberation mechanism (Reaction Deliberation). Then, based on these results of deliberation, the mechanism directs the events to the plans that are already running or those that can be called up from the library plans. The plans, when running, can modify the knowledge base of the agents, send messages to other agents, create new subtargets, and enable internal events. The reaction mechanism and resolution is generally the same for all agents. However, the behavior of a specific agent is determined by its beliefs (Beliefs), goals (Desires) and plans (Intentions). The patterns in JASOF are provided as a set of goals and plans encapsulated as Jadex capabilities, which can be easily imported and implemented by an agent. The environment is modeled as a space composed of interconnected positions following the notion of a grid. Furthermore, it is based on the agents-and-artifacts (A&A) approach (Gardelli et al., 2008), which considers the environment populated by agents and artifacts,
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Figure 2.1: JASOF Class Diagram (Environment)
where artifacts are reactive agents providing resources and services, meaning an active environment. The class diagram in Figure 2.1 shows the structure of the environment, where the class Environment contains information about the available locations, the agents in the system and your neighborhood. These locations are represented by the class Location that provides information about its artifacts (class Artifact), their manager agent and their relative position in the environment (class Position).
2.2.2 JASOF Structure First, we show how the environment is modeled and how it acts in our framework through the Agent Location. Then, the plans provided by JASOF that enable the implementation of the referred patterns are described in detail. Figure 2.2 shows the class diagram that represents these plans and the hotspots from which it is possible to create new patterns not yet documented in the literature. In Figure 2.3 we have the basic JASOF architecture, showing the components of JASOF, described below, and the interconnection between the application and Jadex. 2.2.2.1 Environment In natural self-organizing systems and artificial prototypes, the environment plays a key role in the system dynamics. It is through the interaction with the environment that self-organization of different systems is achieved. A typical example is that of ants foraging, which perform interactions with the environment such as storage of pheromones and their perception, which then guide their decisions and do their work. Therefore, the environmental model is a key
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Figure 2.2: JASOF Class Diagram (Plans)
part of the framework to build such systems. It is from the environment that the patterns of self-organization are possible. Thus, it is available in the JASOF modeled as a set of discrete locations, interconnected to form a grid in two dimensions and, in addition, as mentioned above, is based on the A&A approach, giving it an active environmental characteristic. At
Figure 2.3: JASOF Architecture
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems 25 each position of the environment a location is allocated. It is important to define the difference between them: position refers to a reference point in the environmental space, whereas location is an area located in a position that can be inhabited by multiple agents. In each location there is an agent responsible for its management; this agent is named Agent Location. 2.2.2.2 Agent location As already said, each location in the environment has an agent, called Agent Location, responsible for managing the artifacts stored in this location, to execute the patterns and to provide artifact access to agents that are in the same position. It is through the agent Location that the environment has an active behavior, executing and interacting with the other participants of the system. Those participants are comprised of all agents in the environment, Locations and others. The agents that are not Location are called User agents in the framework. Through communication with the Agent Location of a specific location it is possible for a given agent to insert an artifact, which can represent relevant information such as a pheromone, or to read the stored artifact. All communication with the Agent Location to insert and read is a frozen spot of the framework. The reason for associating agents to each location is justified by the need to search for forms of decentralization, despite the large number of agents and the communication required for reading and writing information. In the following sections the ease the environment and agents have in implementing self-organization patterns can be observed. 2.2.2.3 Diffusion pattern The idea behind the Diffusion Pattern is the process present in nature that when a pheromone is deposited into the environment, it spontaneously tends to diffuse to neighboring locations (Weyns et al., 2008). Diffusion distributes the information equally across all neighboring locations. In order to implement the diffusion pattern it is necessary for the agent to import the DiffusionPattern capability, depicted in Figure 2.4. It provides the infrastructure needed for an agent to send information, decreased by a factor, to a set of neighbors selected according to a rule (PropagationRule) and perform the actions related to the coordination process or react to a received message. This is, in essence, the coordination mechanism. Such capability has the class DiffusionPatternPlan, Figure 2.2, which needs to be extended in order to define the rules for selection of agents to receive the message (method DestinationRule), the actions of a coordination (method CoordinationRule) and the propagation (method PropagationRule) process. As we can see in Figure 2.4, the message diffusionMsg is the standard event used to perform the diffusion pattern, so importing that capability implies that the agent can receive and handle that message. If necessary it is possible to modify the ReceiveRule method and change this process.
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Figure 2.4: Diffusion Capability
2.2.2.4 Evaporation pattern Evaporation can be considered a mechanism to reduce the amount of information, based on a time relevance criterion, avoiding an overload of information. The Evaporation Pattern can be implemented by an agent, using JASOF, by importing the EvaporationPattern capability. Figure 2.5 shows that such capability will periodically execute
Figure 2.5: Evaporation Capability
A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems 27 the evaporate() method, present in the class Artifact, of the instances contained in the Belief Base of the respective agent Location. To change the frequency it is necessary to modify the parameter recurdelay that by default is 6000, as shown in Figure 2.5. Another parameter that can be determined is how much of the information will be evaporated per cycle; in this case the attribute factor of the Artifact class must be changed. 2.2.2.5 Aggregation pattern Aggregation is a mechanism of reinforcement and is also observable in human social tasks (Gardelli et al., 2007). When redundant information is stored, its relevance increases. As in the other patterns, to implement the Aggregation patterns it is necessary to import the EvaporationPattern capability, which captures the concept of Evaporation, periodically checks the artifacts saved in the Belief Base and checks which of them may suffer an aggregation. This aggregation is accomplished by the method aggregate() of the Artifact class. An aggregation process between two artifacts produces one with more relevance. 2.2.2.6 Replication pattern Natural systems typically feature replication mechanisms in order to increase security and robustness. The Replication pattern works in a way similar to the Diffusion pattern; the main difference resides in the factor of the information replicated. In the diffusion case, the artifact suffers a decrease in the factor attribute and, on the other hand, the replication does not modify that attribute, keeping its actual value. The mechanisms DestinatioRule, CoordinationRule, and PropagationRule are used for the same purpose.
2.2.3 JASOF Hotspots The framework hotspots are: 1. The ability to specify the information to be used in the communication process: Using the class Artifact it is possible to create new types of data to be stored and exchanged by agents. 2. Pattern composition for the creation of new self-organizing mechanisms: Through Jadex features it is possible to include several plans in an agent, creating the potential for patterns composition. 3. The creation of new basic patterns: Flexibility through the extension of the PatternPlan class and the implementation of its respective plan. 4. Coordination and propagation mechanisms to be specified in each pattern: It is possible to build a self-organizing solution on the basis of ant foraging or in a Gossip Protocol, just modifying this mechanism.
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2.3 Case Study: A Self-Organized Automatic Guided Vehicle Automated guided vehicles (AGVs) are fully automated, custom-made vehicles that are able to transport incoming loads (i.e., packets, materials, and/or products) in a logistical or production factory environment (Weyns and Holvoet, 2008; Weyns et al., 2008). AGV systems can be used for distributing manufactured products to storage locations or as an inter-process system between various production machines. An AGV system receives transport requests for loads from a factory management system or machine operating software, and instructs AGVs to execute transports. We have used the JASOF framework to build a self-organizing multiagent system in order to implement a decentralized solution to the AGV problem. Furthermore, the system can withstand perturbations, such as unavailable routes or barriers in the path.
2.3.1 Main Idea In order to implement a solution to this problem we have modeled the AGV system using the JASOF framework with four agents: (1) Destination Agent, representing the destination; (2) Warehouse Agent, representing the warehouse; (3) Transporter Agent, representing the AGV; (4) Location Agent, representing the Stations between the warehouses and the destinations. As we can see in Figure 2.6, which illustrates a reduced situation, the architecture is modeled as follows: the locations where packages must be picked up are located on the left (Warehouse), the destinations on the right (Destination), while the network in between consists of Locations and connecting segments on which AGVs are located (AGV Location). Each individual AGV (Transporter) is capable of only a limited set of local Actions, such as Move, Pick Load and Drop Load. The vehicles move loads in a factory by moving towards the warehouse location: picking up the load, moving towards the destination location, and dropping the load. The fleet of AGVs has to self-organize efficiently to accommodate a current request for transport and to withstand unpredictable situations. In the next subsection we present more details about such agents.
Figure 2.6: AGV Environment Structure
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2.3.2 Destination Agent In our solution using the framework, this agent is responsible for receiving the packages transported by the Transporter agent and to inform, using the diffusion pattern, the neighborhood of what it is capable of doing. This information is communicated to the Location agents that are interconnected by one segment, excluding the other types of agents. This agent was implemented as a Jadex agent importing the DiffusionPattern capability of the framework. This pattern has a plan with four mechanisms of extensibility: receive, destination, coordination, and propagation. Receive and destination mechanisms were not modified though. The standard solution was adopted. However, the DestinationRule was extended, regulating that only agents Location were able to receive the diffusionMsg. Furthermore, the PropagationRule was extended to determine the type of information to be diffused. Complementing the functionality of the Destination agent, a new plan was created to define the protocol specifying the messages used to receive new packages from the Transporter agent.
2.3.3 Warehouse Agent This agent is responsible for managing the presence of packages in its location. When a new package arrives, the agent runs the diffusion pattern to propagate the information in the environment, advising that a new load is available and ready to be transported. As the Destination agent, the mechanisms ReceiveRule and CoordinationRule have been maintained as the default option and the DestinationRule and PropragationRule were modified for the same purpose. The information diffused contains the position of the Destination agent in the environment, used by the agent Transporter to build its route.
2.3.4 Transporter Agent This agent represents the AGV, responsible for transporting packages from warehouses to their destinations. Their actions are: Move, Pick Load and Drop Load. Each action was modeled as a step in a recurrent goal: while the Transporter is not loaded with a package to be transported, the agent will move (action Move) past its neighbors for information about locations that may eventually have a package to be transported; this movement is performed until the agent finds a package. In our solution the battery was not considered a problem. Once a package is found, the Transporter agent carries the load (Pick Load) and then the plan to deliver the package is triggered. The agent will travel through the environment looking for the destination and will build a route collecting the information available in the locations. When the agent finds the destination, a message is sent to the Destination agent at that position to dispatch the package. Finally, the process is restarted.
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2.3.5 Location Agent On this occasion, the Location agent performs the Evaporation, Aggregation, and Diffusion patterns. The Evaporation pattern, as seen in the Section 1.2.2.4, occurs periodically performing the decrement of the relevance factor from the information stored in the location. It is the Aggregation. However, in this case, aggregation is performed between similar information stored. The Diffusion pattern occurs when the diffusionMsg received from other agents has the parameter path higher than 0, indicating that the information has to be diffused again.
2.3.6 Execution The execution can be described through the six events that occur. They are: 1. Initializing the environment; the locations are put in their positions and their respective Location agents are instantiated as well as the agents Warehouse and Destination. As we can see in Figure 2.7. 2. As soon as the Destination agent is created, the diffusion process starts by sending information to the neighborhood about its availability and position. As we can see in Figure 2.8. 3. Adding a package – when a package is loaded in a Warehouse, the responsible agent starts to diffuse to the neighborhood that a new package is waiting to be carried out. As shown in Figure 2.9.
Figure 2.7: Initializing the Environment
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Figure 2.8: Diffusion Process
4. While these events occur, the Location agents have already started the evaporation process. The ones near the agent Destination and Warehouse evaporate the recent information about position and package, respectively. 5. The Transporter agent is looking for information about available packages to be picked up. It is through the agents Locations that it builds its route, as we can see in Figure 2.10. 6. When the Transporter agent finds a package, it starts to look for information about destinations, similar to the step described above. When the destination is found the Transporter agent reinitializes the search for another package.
Figure 2.9: Warehouse diffusing a package availability
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Figure 2.10: Dispatch Process
2.3.7 System Composition A relevant motivation for the separation of the mechanisms of self-organization in simple patterns is the resulting possibility of forming more sophisticated and undiscovered ones, from just a simple composition of patterns. This separation is one of the main challenges in engineering self-organizing systems, as discussed in (Parunak and Brueckner, 2011). As noted, the JASOF framework allows the construction of agents that perform specific mechanisms of self-organization and their combination. Also, as the case study illustrates, we can have agents with different mechanisms interacting in the system, and the result of this composition can be the emergence of known high-level mechanisms, like stigmergy or gradient fields, or even others not yet described in the literature. The AGV case study was modeled on the basis of the construction of a system supported by indirect communication among the Transporters through the environment, using the notion of pheromones. We used the standard Evaporation, Diffusion, and Aggregation patterns in Location entities. However, our solution to the problem in finding AGV routes could be based on the gradient fields idea, where from these same patterns we could compose a new solution, just changing the hotspots points: mainly the propagation method and the Artifact class. Another possible self-organizing solution for the AGV problem, using the JASOF framework, could be through the Gossip protocol that has been shown to be robust in the presence of failures of individual nodes (Gavidia et al., 2006). As an example, it has been used in wireless mesh networks to propagate a news device, or to disseminate state information with the purpose of building hierarchies of clusters in wireless sensor networks (Iwanicki and van Steen, 2009).
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2.4 Related Work In (De Wolf and Holvoet, 2006), two of the main decentralized coordination mechanisms are described in a structured manner. They are the gradient field and market-based control. These mechanisms are dealt with as patterns to be adopted when searching for a decentralized organization solution. Furthermore, (De Wolf, 2007) describes how these patterns need to be adopted and the consequences of their adoption. Nevertheless, the pattern descriptions are only described at the conceptual level, only allowing the formulation of high-level solutions. The authors are not concerned about the implementation aspects, or in supporting the software development, that is, how to structure the self-organization system using agents and classes. Following the same approach as De Wolf (De Wolf and Holvoet, 2006), (Gardelli et al., 2007) describes the above mentioned patterns in terms of low-level patterns, which can be called basic patterns. They are: Collective Sort, Diffusion, Aggregation, Replication, and Evaporation. However, the approach used is the same and there is still no concern with the implementation of these patterns.
2.5 Conclusions and Future Work In this chapter we presented JASOF, a pattern-based framework that provides built-in selforganizing patterns implemented following the BDI architecture. Self-organizing patterns have only been described so far (Gardelli et al., 2007; De Wolf and Holvoet, 2006; Babaoglu et al., 2006) as a very high level of abstraction without any indication of how they could be implemented. In our proposed solution, self-organizing patterns modularized into capabilities are the basic building block to construct self-organizing multiagent systems or new self- organizing patterns. The JASOF approach was illustrated using the AGV case study. The AGV encompasses three different agents: (1) Location –- implements the evaporation, diffusions, and aggregation; (2) Warehouse – implements the Diffusion pattern; and (3) Destination – implements the Diffusion pattern. This case study shows the viability of JASOF for the implementation of complex and decentralized problems, in addition to illustrating how such problems can have a facilitated solution by the composition of self-organizing patterns and simply implemented using the BDI architecture or, more specifically, using agents goals and plans modularized into Jadex capabilities. This framework is a first step towards a complete infrastructure where complex and decentralized systems can be automatically generated from high-level specifications, such as domainspecific languages. As a future work, we intend to validate our ideas in more complex case studies and suggest some guidelines to help the creation of new self-organizing patterns using our proposed infrastructure.
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Acknowledgment This work is supported by the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ.
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