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LIVING SYSTEM THEORY AS A CYBERNETIC MODEL FOR DESIGN AND ANALYSIS OF ADAPTIVE HUMAN-COMPUTER INTERFACE
LIVING SYSTEM THEORY AS A CYBERNETIC MODEL FOR DESIGN AND ANALYSIS OF ADAPTIVE HUMAN-COMPUTER INTERFACE Celestine A. Ntuen and Emmanuel Letsu-Dake
Center for Human-Machine Studies North Carolina A &T State University Greensboro, NC 27411, USA [email protected] [email protected]
Abstract: This paper presents the Living System Theory as a cybernetic model and a tool in which biological methods of adaptation are used to design an adaptive humancomputer interface. The rationale is that living systems, particularly humans, adapt to situations based on their goals and objectives. At a high level of information abstraction, it can be reasonably deduced that an interface which adapts in a manner similar to or consistent with living systems would be equally successful in emulating changes in user characteristics, tasks and situations. A comparison of LST to other HCI relevant cybernetic models is made—particularly to social cybernetics and perceptual control theory. Copyright © 2007 IFAC Keywords: Interfaces, adaptive systems, human-machine interface, cybernetics.
1. INTRODUCTION Human-computer interaction (HCI) models or systems serve to mediate the interaction between human and machine (computer) by facilitating information exchange. With increasing complexities in systems that utilize HCI, the role of interfaces has assumed greater importance, one particular point being the ability of the interface features or elements to adapt to tasks and user characteristics. Because of this, adaptive interfaces have attracted considerable discourse in recent times. In particular, adaptive HCI (AHCI) are needed in task situations where humans interact with other agents within a complex (and adaptive) system. Such systems are known to be: (1) dynamic - because previous and/or current actions determine subsequent states of the system; (2) timedependent - because decisions must be made at the correct moment in relation to environmental
demands; and (3) complex - because variables are not related to each other in a one-to-one manner but rather in one-to-many or many-to-many interactions (Quesada, Kintsch et al. 2001; Quesada, Kintsch et al. 2005). Examples of such complex systems are nuclear power plants, petrochemical plants and net-work centric military information management systems. Literature on adaptive interfaces is diverse due to the varied approaches used and the task contexts in which they are developed and applied. Five relevant definitions are presented here. First, adaptive systems are systems that can alter aspects of their structure, functionality or interface in order to accommodate the differing needs of individuals or groups of users and the changing needs of users over time (Benyon, Innocent et al. 1987). Second, it can be defined as an interactive software system that improves its ability to interact with a user based on partial interactions
with that user (Langley 1997). Third, Keeble and Macrebie (2000) define an adaptive interface as one in which the appearance, function or content of the interface can be changed by the interface (or underlying application) itself in response to the users interaction with it. Fourth, an adaptive interface is one that autonomously adapts its displays and available actions to current goals and abilities of the user by monitoring user status, the system task, and the current situation (Rothrock, Koubek et al. 2002). Fifth, according to Zudilova-Seinstra (2007), adaptive user interfaces allow the interactive environment to automatically learn and adapt to important user, task and environmental parameters. Rothrock, Koubek et al (2002) argue that real-time adaptability poses the greatest challenge to interface designers because their features are required to be sensitive along the dimensions of user characteristics, tasks, and situations. Thus, different tasks emphasize different adaptive interface requirements. It is observed that the existing methodologies of designing AHCI are not sufficiently robust to accommodate all three dimensional requirements of the user, task, and changing situations. Thus, there are shortfalls in the existing approaches. Among these are the lack of rigorous analytical methods that can evaluate and/ or recognize the instances of adaptation. For example, pertinent usability design issues may include what triggers adaptation, when the adaptation should occur, why the interface is adapting to a situation, and how the adaptation process enables task performance. The Living Systems Theory (LST) offers some robust solutions to the above design problems. 2. OVERVIEW OF LST LST is an integrated conceptual approach to the study of biological and social living systems, the technologies associated with them, and the ecological systems of which they are all parts (Miller and Miller 1995). The LST process provides a conceptual framework for identifying and defining essential components (levels) and processes (subsystems) of systems both living and non-living and the relations between them. It looks at systems from the standpoint of their structures, their processes, their subsystem processes, and the relations between subsystems. Using the concept of hierarchical levels, LST organizes the universe into eight (8) hierarchical levels or abstractions, each with a characteristic structure and process. LST identifies and defines twenty (20) critical subsystems at each level of hierarchy which are necessary for system survival. LST as a cybernetic model can be used to design conceptual models to control a system of interest through hierarchical decomposition of many finely adjusted, interlocking processes involving transmissions of matter-energy and information (Miller, 1978, pg. 36). LST can be described as being comprised of intrinsic cybernetic units with reference signals which are based on: (i) the system goals; (ii)
input functions that monitor the state of the system; and (iii) output functions that change the state of the system. The individual behaviours in the LST model relate to the low level subsystems of LST (e.g., cell, organ, and organism), organizational behaviours (e.g. computer-supported cooperative work) and distributed intelligence related to the middle sections of the HCI hierarchy. The eight levels of living systems are: Cells: The basic building block of life. Organs: The principle components are cells, organized in simple, multi-cellular systems. Organisms: This level includes multi-cellular plant and animal life forms. The components of organisms are organs. Each organism carries out life processes differently. Groups: These contain two or more organisms interacting as systems and their relationships. Organizations: These involve one of more groups with their own control systems for doing work. They differ from groups due to the echelon in their decision structures. Communities: They include individual persons and groups, as well as groups which are formed and are responsible for governing or providing services to them. Societies: These are loose associations of communities, with systematic relationships between and among them. Supranational systems: These systems are composed of two or more societies which undertake cooperative decision making. They have a supra-ordinate system of influence and control. Each level of the hierarchy performs certain critical processes in order to remain alive and continue beyond a single generation. These processes are known as subsystems. LST identifies and defines twenty (20) critical subsystems. Two subsystems process matter-energy and information, eight process matter-energy and ten process information. Miller (1995) lists the 20 essential subsystems which are not discussed here (The reader is referred to the citation). 3. APPLICATION TO POWER SYSTEMS INTERFACE DESIGN LST as a cybernetic model for the design of adaptive human-computer interfaces can be described using the model shown in Figure 1. LST describes the adaptation mechanism in four stages: i. ii. iii.
Establishing the purpose or goal whose achievement is to be advanced by the adaptation; Synthesizing the information relevant to the adaptation decision; Analyzing a solution set for selecting the alternative action(s) most likely to lead to the purpose or goal of adaptation;
Mechanism Associator Synthesize
Analyze
Memory
Table 1. LST Information for a Power Plant Interface Design
Decider Store
Decoder
Encoder
Input Transducer
Output Transducer
Trigger
Component
Description
Input Transducer
Brings information into the adaptive interface. It is the sensory apparatus for receiving signals from the environment. Alters information from the input transducer by changing it into forms suitable for transmission and analysis by the adaptive interface. Associates input with existing patterns, models in memory using a reward/penalty system. Stores inputs and associated actions for future reference. Executive subsystem which outputs course(s) of action to control the adaptive interface. Transforms internal information into forms that can be interpreted by others systems in the environment Outputs internal information to the environment.
Effector
Process & Operator Variables
Adaptive Display
Controls: Adaptive Automation
Fig 1. A Cybernetic Model for AHCI Using LST iv.
Making an adaptation decision, storing the decision in memory and issuing a command signal to carry out the action(s).
An adaptation trigger is required to initiate the adaptation process and effectors are required to implement adaptation actions. Triggers are activated when process or operator variables deviate from set steady states. The steady states or comparison signals may be from mathematical models of the system, previous knowledge or expert design decisions. In the case of the power systems interface, process flow values and operator error rates can be used as triggers. Any discrepancy between the present state of a variable and the comparison signal indicating the appropriate steady-state value for that variable requires an adaptation action. Adjustment processes are used to correct deviations through effectors. Effectors are allowable modifications on the current state of the interface or process. In this case, they may range from changing the information presented to the operator or the form in which it is presented, to automation of the process control. The mechanism refers to the artificial intelligence implementation of the associator-memory-decider of LST. Finite state automata and neural networks are possible implementations. Based on the results of adaptation actions, a reward or penalty is awarded by the mechanism. From biological analogy, a system which survives generally decides to employ the least costly adjustment to a deviation from steady state first before increasingly considering more costly ones later. This forms the basis of a reward-penalty system. The components of the model and their implementation with respect to a power systems interface are described in table 1.
4. A COMPARISON OF LST TO OTHER RELEVANT CYBERNETIC MODELS Unlike the existing adaptive interface frameworks, LST is inherently decentralized, since as a biological system, its focus is on the emergence of complex behaviors from interactions between relatively simplistic organisms. It can be used to model both human and computer behaviors in a HCI system. Most of the existing adaptive interfaces either model adaptation based on the task, user or situation. LST
Decoder
Associator
Memory Decider
Encoder
Output Transducer Process & Operator Variables
Process of subsystems which maintain steady states in systems, keeping variables within their ranges of stability despite stresses
Power Systems Interface Implementation Process sensors, input devices such as keyboard, mouse. Physiological input devices such as EEG. Input and interface cards, associated software for transforming inputs.
Artificial intelligence algorithms such as neural network, finite state automaton, fuzzy decision system or genetic algorithm.
Output device cards and associated software. Output devices such as computer display, print out or audio alarm. For the process, water flow rate and temperature. For operator, error rate, types of faults and workload.
offers a framework for integrating all three facets of interface features through abstract levels of information association and hierarchy. For example, LST provides abstract control strategies for specifying the order in which interaction rules are applied, as well as rules for conflict resolution similar to the selection process in biological systems. It also offers a mechanism to evaluate the elements of the object space and determine the effectiveness of rule application, similar to fitness for survival in a biological system.
Other aspects of cybernetics which are relevant to human-computer interaction relate to behavioral cybernetics interpreted in terms of perceptual control theory by Powers (1973; 1989) and social cybernetics (Smith & Smith, 1988; Smith & Smith, 1987). Powers (1973) is credited with developing perceptual control theory (PCT) - a theory that asserts that behavior is the control of perception rather than the response to a stimulus. These can be human behaviors while interacting with the system, or the system behaviors responding to input-output processes, functionalities, or energy exchange during interaction of the elements and components of the system, and other trigger mechanisms often attributed to the environment—the so-called ecological niche. PCT provides dynamic working models based on the principle that goal-directed activity arises from a hierarchy of negative feedback loops that control perception through control of the environment. The theory proposes that psychological distress arises from the unresolved conflict between goals. For example, in the HCI domain, the human is required to know nominally every potential variable that is necessary for task performance (e.g. selecting or setting of variable from a menu). Similarly, the computer (or generally the machine) is designed to recognize variables that drive the system behavior (e.g.: in a nuclear power plant, even the non-adaptive elements of HCI should be designed to recognize and shut down temperature or pressure valves based on control rules artificially designed into the HCI engine. Similar to LST, nine levels are proposed in the description of PCT by Powers (1973, 1992). In PCT, the major tasks consist of perceiving, comparing, and acting—leading to an embodiment of perceptual and cognitive functions. These functions are encountered in every aspect of HCI, or at least, some form of it. Intrinsic to the evaluation aspect of PCT are the mediating factors of the environment and the closed loop connection of system disturbance (known as error in control theory). Smith and Smith (1988) described the social cybernetic theory of human-computer interaction and explored its implications. The social cybernetic approach is based upon the premise that human interaction with automated systems embodies, in many significant respects, the reciprocal communicative and performance interchanges which characterize social interaction among humans. It propounds that behavior is a closed loop, selfregulated process mediated by motor feedback control (Smith and Smith, 1987, 1988). It further assumes that social interaction like all other biological activities is feedback controlled; similar to Powers (1973), except Smith and Smith pay more attention to the granular scales of sensorimotor performance. Figure 2, adapted from Smith and Smith (1988), portrays the general conception of the social cybernetics model.
Sensory Feedback
Sensor Sensory Feedback
Effector Sensory Feedback Control
Human-Computer Interaction
COMPUTER Output Feedback Control
Effector Feedback
Output Feedback
Output Device
Effector Feedback Control
PERSON
Sensory Feedback Control
Sensory Feedback
Receptor Sensory Feedback
Fig 2. Social cybernetic model of human-computer interaction (Smith & Smith, 1988; pp.701) 4.1 A Summary of the three cybernetic models The following properties and capabilities of LST are described in Miller (1978): 1.
2.
3.
4. 5.
It is a framework of basic concepts which is capable of organizing complex properties of It is a framework of basic concepts which is capable of organizing complex properties of systems in a rational and unified way. It can help to identify variables that have not been studied, as well as gaps in knowledge of systems at any given level. It can also reveal over or under-emphasis on certain adjustment processes. If research at one level establishes reliable relationships among variables or derives a useful indicator, similar measurements can be made at other levels in order to see whether they can enhance analysis at that level. Once a steady-state range has been determined at one level, its range may be estimated for a higher or a lower level. It can also aid in recognizing previously unseen relationships between a set of research findings at the same or different levels within different domains. Thus it facilitates the generalization of systems of all sorts.
In the perceptual control theory (PCT) developed by Powers (1973), some fundamental characteristics and assumptions are made consistent with physiological evidence (lowest levels) and experience (conscious perception). The nine levels proposed by Powers (1973, 1992) are as follows: 1. 2. 3. 4.
Intensity such as the magnitude of alarm in a nuclear power plant. Sensation or vector which consists of the first level experience attributed to the variables in the system. Configuration that stipulates the topology of information flow in the system. Transitions which address the changes of matter and energy from one level of perceptual experience (such as changes in display modality) to another.
5. 6.
7.
8.
9.
Sequence of behavioral primitives that are intentionally formed for specific acts rather than response to stimuli. Relationship that defines the strengths of relationships between effectors and sensors in the physiological apparatus controlling specific behaviors. Program which is a sort of automaton that regulates the effect of environment in control variables based on the system disturbance generated by the system. Principles which represent a set of interlocking theories and paradigms from behavioral psychology and system theory. For example, Powers (1973) alludes to the fact that what we perceive does not control us. It is our reference levels that originate within us that control our perception. This principle, for example, has direct link to Gibson’s (1979) ecological principle of direct perception. System concepts that describe concepts and abstractions in terms of a collective body of knowledge reviewed to show some commonality and reliance to the thematic theory. For example, Powers views perception-action continuum as a hierarchical organization of control systems in which each level of the hierarchy controls a different class of perception.
The social cybernetics approach (Smith and Smith, 1988) theorizes that group behaviors generate a closed-loop network of reciprocative, communicative and interactive acts. The control variables for such systems are controlled by mutual, dynamic, and adaptive exchange of many sensory feedback modalities. As noted by Smith and Smith (1988): “The field of behavioral cybernetics is founded upon the basic postulate that behavior is a closed-loop, self-regulated process mediated by motor feedback control” (pp. 692). As with LST and PCT, social cybernetics has both behavior and physiological variables mutually integrated. However, social cybernetics emphasizes the aspects of distributive mutual control and social interaction of the “energy-matter” transformation process. This is illustrated by the sensor-effector information exchange. Implicitly, out of these interactions, cooperation, behavioral coordination, and mutual understandings co-evolve. From the social cybernetics perspective, HCI may require the computer to have basic capacities for perception, action, and concept formation that can be shared with other computers (machines) and humans who interact with them. This requires the designers’ understanding of the semiotics of the cybernetic agents. Examples of these are described by Rasmussen (1986) as signs, signals, and symbols, or in the design rationale by a three-tier level of communication acts that involve syntactic, semantic, and/or pragmatic levels, respectively.
5. CONCLUSION The underlying purpose of this paper is to present LST as a cybernetic model for design and analysis of an adaptive HCI design. The primary tenet of discussion emphasizes that by drawing from the biologically inspired features of LST, user interface models can be developed to acquire adaptation. The advantage of this view is that it leads to a more flexible system with robust performance in a dynamic environment. This kind of HCI design system can anticipate the impact of a change in one information element or system level on the performance of the system. Defined in terms of a generic biological system, the LST framework can be used to recognize, in advance, the unanticipated events in the system. Thus, uncertain emergence system behaviors can be represented in HCI design. This will help to facilitate adaptive behaviours in HCI. Finally, the integration of soft-computing techniques like genetic algorithms, fuzzy logic and artificial neural networks into the LST-HCI paradigm for optimization, search and classification processes can ensure efficient selflearning and adaptability.
REFERENCES Bailey, K. D. (2006). Living systems theory and social entropy theory. Systems Research and Behavioral Science, 23(3), 291-300. Benyon, D., P. Innocent, et al. (1987). System Adaptivity and the Modelling of Stereotypes, National Physical Laboratory, Division of Information Technology and Computing. Keeble, R. J. and R. D. Macredie (2000). Assistant agents for the world wide web intelligent interface design challenges. Interacting with Computers, 12(4), 357-381. Langley, P. (1997). Machine Learning for Adaptive User Interfaces, Daimler-Benz Research & Technology Center. Miller, J. G. (1978). Living Systems. Niwot, CO, University Press of Colorado. Miller, J. G. and J. L. Miller (1995). Applications of living systems theory. Systemic Practice and Action Research, 8(1),19-45. Powers, W. T. (1973). Behavior: The Control of Perception, Aldine, Chicago. Powers, W. T. (1992). Living control systems II, Gravel Switch, KY: The Control Systems Group. Quesada, J., W. Kintsch, et al. (2005). Complex problem-solving: a field in search of a definition? Theoretical Issues in Ergonomics Science, 6(1), 5-33. Quesada, J. F., W. Kintsch, et al. (2001). A computational theory of complex problem solving using the vector space model (part I): Latent Semantic Analysis, Through the Path of Thousands of Ants. In: Proceedings of
the 24th Annual Conference of the Cognitive Science Society., Fairfax, VA., Lawrence Erlbaum Associates, Mahwah, NJ. Rothrock, L., R. Koubek, et al. (2002). Review and reappraisal of adaptive interfaces: toward biologically inspired paradigms. Theoretical Issues in Ergonomics Science, 3(1): 47-84. Smith, T. J. and K. U. Smith (1988). The social cybernetics of human interaction with automated systems. In: Proceedings of the First International Conference on Ergonomics of Hybrid Automated Systems I (W. Karwowski, H.R. Parsaei, & M.R. Wilhelm, pp. 691-711). New York: Elsevier. Zudilova-Seinstra, E. V. (2007). On the Role of Individual Human Abilities in the Design of Adaptive User Interfaces for Scientific Problem Solving Environments. Knowledge and Information Systems Int. J.
Center for Human-Machine Studies North Carolina A &T State University Greensboro, NC 27411, USA [email protected] [email protected]
Abstract: This paper presents the Living System Theory as a cybernetic model and a tool in which biological methods of adaptation are used to design an adaptive humancomputer interface. The rationale is that living systems, particularly humans, adapt to situations based on their goals and objectives. At a high level of information abstraction, it can be reasonably deduced that an interface which adapts in a manner similar to or consistent with living systems would be equally successful in emulating changes in user characteristics, tasks and situations. A comparison of LST to other HCI relevant cybernetic models is made—particularly to social cybernetics and perceptual control theory. Copyright © 2007 IFAC Keywords: Interfaces, adaptive systems, human-machine interface, cybernetics.
1. INTRODUCTION Human-computer interaction (HCI) models or systems serve to mediate the interaction between human and machine (computer) by facilitating information exchange. With increasing complexities in systems that utilize HCI, the role of interfaces has assumed greater importance, one particular point being the ability of the interface features or elements to adapt to tasks and user characteristics. Because of this, adaptive interfaces have attracted considerable discourse in recent times. In particular, adaptive HCI (AHCI) are needed in task situations where humans interact with other agents within a complex (and adaptive) system. Such systems are known to be: (1) dynamic - because previous and/or current actions determine subsequent states of the system; (2) timedependent - because decisions must be made at the correct moment in relation to environmental
demands; and (3) complex - because variables are not related to each other in a one-to-one manner but rather in one-to-many or many-to-many interactions (Quesada, Kintsch et al. 2001; Quesada, Kintsch et al. 2005). Examples of such complex systems are nuclear power plants, petrochemical plants and net-work centric military information management systems. Literature on adaptive interfaces is diverse due to the varied approaches used and the task contexts in which they are developed and applied. Five relevant definitions are presented here. First, adaptive systems are systems that can alter aspects of their structure, functionality or interface in order to accommodate the differing needs of individuals or groups of users and the changing needs of users over time (Benyon, Innocent et al. 1987). Second, it can be defined as an interactive software system that improves its ability to interact with a user based on partial interactions
with that user (Langley 1997). Third, Keeble and Macrebie (2000) define an adaptive interface as one in which the appearance, function or content of the interface can be changed by the interface (or underlying application) itself in response to the users interaction with it. Fourth, an adaptive interface is one that autonomously adapts its displays and available actions to current goals and abilities of the user by monitoring user status, the system task, and the current situation (Rothrock, Koubek et al. 2002). Fifth, according to Zudilova-Seinstra (2007), adaptive user interfaces allow the interactive environment to automatically learn and adapt to important user, task and environmental parameters. Rothrock, Koubek et al (2002) argue that real-time adaptability poses the greatest challenge to interface designers because their features are required to be sensitive along the dimensions of user characteristics, tasks, and situations. Thus, different tasks emphasize different adaptive interface requirements. It is observed that the existing methodologies of designing AHCI are not sufficiently robust to accommodate all three dimensional requirements of the user, task, and changing situations. Thus, there are shortfalls in the existing approaches. Among these are the lack of rigorous analytical methods that can evaluate and/ or recognize the instances of adaptation. For example, pertinent usability design issues may include what triggers adaptation, when the adaptation should occur, why the interface is adapting to a situation, and how the adaptation process enables task performance. The Living Systems Theory (LST) offers some robust solutions to the above design problems. 2. OVERVIEW OF LST LST is an integrated conceptual approach to the study of biological and social living systems, the technologies associated with them, and the ecological systems of which they are all parts (Miller and Miller 1995). The LST process provides a conceptual framework for identifying and defining essential components (levels) and processes (subsystems) of systems both living and non-living and the relations between them. It looks at systems from the standpoint of their structures, their processes, their subsystem processes, and the relations between subsystems. Using the concept of hierarchical levels, LST organizes the universe into eight (8) hierarchical levels or abstractions, each with a characteristic structure and process. LST identifies and defines twenty (20) critical subsystems at each level of hierarchy which are necessary for system survival. LST as a cybernetic model can be used to design conceptual models to control a system of interest through hierarchical decomposition of many finely adjusted, interlocking processes involving transmissions of matter-energy and information (Miller, 1978, pg. 36). LST can be described as being comprised of intrinsic cybernetic units with reference signals which are based on: (i) the system goals; (ii)
input functions that monitor the state of the system; and (iii) output functions that change the state of the system. The individual behaviours in the LST model relate to the low level subsystems of LST (e.g., cell, organ, and organism), organizational behaviours (e.g. computer-supported cooperative work) and distributed intelligence related to the middle sections of the HCI hierarchy. The eight levels of living systems are: Cells: The basic building block of life. Organs: The principle components are cells, organized in simple, multi-cellular systems. Organisms: This level includes multi-cellular plant and animal life forms. The components of organisms are organs. Each organism carries out life processes differently. Groups: These contain two or more organisms interacting as systems and their relationships. Organizations: These involve one of more groups with their own control systems for doing work. They differ from groups due to the echelon in their decision structures. Communities: They include individual persons and groups, as well as groups which are formed and are responsible for governing or providing services to them. Societies: These are loose associations of communities, with systematic relationships between and among them. Supranational systems: These systems are composed of two or more societies which undertake cooperative decision making. They have a supra-ordinate system of influence and control. Each level of the hierarchy performs certain critical processes in order to remain alive and continue beyond a single generation. These processes are known as subsystems. LST identifies and defines twenty (20) critical subsystems. Two subsystems process matter-energy and information, eight process matter-energy and ten process information. Miller (1995) lists the 20 essential subsystems which are not discussed here (The reader is referred to the citation). 3. APPLICATION TO POWER SYSTEMS INTERFACE DESIGN LST as a cybernetic model for the design of adaptive human-computer interfaces can be described using the model shown in Figure 1. LST describes the adaptation mechanism in four stages: i. ii. iii.
Establishing the purpose or goal whose achievement is to be advanced by the adaptation; Synthesizing the information relevant to the adaptation decision; Analyzing a solution set for selecting the alternative action(s) most likely to lead to the purpose or goal of adaptation;
Mechanism Associator Synthesize
Analyze
Memory
Table 1. LST Information for a Power Plant Interface Design
Decider Store
Decoder
Encoder
Input Transducer
Output Transducer
Trigger
Component
Description
Input Transducer
Brings information into the adaptive interface. It is the sensory apparatus for receiving signals from the environment. Alters information from the input transducer by changing it into forms suitable for transmission and analysis by the adaptive interface. Associates input with existing patterns, models in memory using a reward/penalty system. Stores inputs and associated actions for future reference. Executive subsystem which outputs course(s) of action to control the adaptive interface. Transforms internal information into forms that can be interpreted by others systems in the environment Outputs internal information to the environment.
Effector
Process & Operator Variables
Adaptive Display
Controls: Adaptive Automation
Fig 1. A Cybernetic Model for AHCI Using LST iv.
Making an adaptation decision, storing the decision in memory and issuing a command signal to carry out the action(s).
An adaptation trigger is required to initiate the adaptation process and effectors are required to implement adaptation actions. Triggers are activated when process or operator variables deviate from set steady states. The steady states or comparison signals may be from mathematical models of the system, previous knowledge or expert design decisions. In the case of the power systems interface, process flow values and operator error rates can be used as triggers. Any discrepancy between the present state of a variable and the comparison signal indicating the appropriate steady-state value for that variable requires an adaptation action. Adjustment processes are used to correct deviations through effectors. Effectors are allowable modifications on the current state of the interface or process. In this case, they may range from changing the information presented to the operator or the form in which it is presented, to automation of the process control. The mechanism refers to the artificial intelligence implementation of the associator-memory-decider of LST. Finite state automata and neural networks are possible implementations. Based on the results of adaptation actions, a reward or penalty is awarded by the mechanism. From biological analogy, a system which survives generally decides to employ the least costly adjustment to a deviation from steady state first before increasingly considering more costly ones later. This forms the basis of a reward-penalty system. The components of the model and their implementation with respect to a power systems interface are described in table 1.
4. A COMPARISON OF LST TO OTHER RELEVANT CYBERNETIC MODELS Unlike the existing adaptive interface frameworks, LST is inherently decentralized, since as a biological system, its focus is on the emergence of complex behaviors from interactions between relatively simplistic organisms. It can be used to model both human and computer behaviors in a HCI system. Most of the existing adaptive interfaces either model adaptation based on the task, user or situation. LST
Decoder
Associator
Memory Decider
Encoder
Output Transducer Process & Operator Variables
Process of subsystems which maintain steady states in systems, keeping variables within their ranges of stability despite stresses
Power Systems Interface Implementation Process sensors, input devices such as keyboard, mouse. Physiological input devices such as EEG. Input and interface cards, associated software for transforming inputs.
Artificial intelligence algorithms such as neural network, finite state automaton, fuzzy decision system or genetic algorithm.
Output device cards and associated software. Output devices such as computer display, print out or audio alarm. For the process, water flow rate and temperature. For operator, error rate, types of faults and workload.
offers a framework for integrating all three facets of interface features through abstract levels of information association and hierarchy. For example, LST provides abstract control strategies for specifying the order in which interaction rules are applied, as well as rules for conflict resolution similar to the selection process in biological systems. It also offers a mechanism to evaluate the elements of the object space and determine the effectiveness of rule application, similar to fitness for survival in a biological system.
Other aspects of cybernetics which are relevant to human-computer interaction relate to behavioral cybernetics interpreted in terms of perceptual control theory by Powers (1973; 1989) and social cybernetics (Smith & Smith, 1988; Smith & Smith, 1987). Powers (1973) is credited with developing perceptual control theory (PCT) - a theory that asserts that behavior is the control of perception rather than the response to a stimulus. These can be human behaviors while interacting with the system, or the system behaviors responding to input-output processes, functionalities, or energy exchange during interaction of the elements and components of the system, and other trigger mechanisms often attributed to the environment—the so-called ecological niche. PCT provides dynamic working models based on the principle that goal-directed activity arises from a hierarchy of negative feedback loops that control perception through control of the environment. The theory proposes that psychological distress arises from the unresolved conflict between goals. For example, in the HCI domain, the human is required to know nominally every potential variable that is necessary for task performance (e.g. selecting or setting of variable from a menu). Similarly, the computer (or generally the machine) is designed to recognize variables that drive the system behavior (e.g.: in a nuclear power plant, even the non-adaptive elements of HCI should be designed to recognize and shut down temperature or pressure valves based on control rules artificially designed into the HCI engine. Similar to LST, nine levels are proposed in the description of PCT by Powers (1973, 1992). In PCT, the major tasks consist of perceiving, comparing, and acting—leading to an embodiment of perceptual and cognitive functions. These functions are encountered in every aspect of HCI, or at least, some form of it. Intrinsic to the evaluation aspect of PCT are the mediating factors of the environment and the closed loop connection of system disturbance (known as error in control theory). Smith and Smith (1988) described the social cybernetic theory of human-computer interaction and explored its implications. The social cybernetic approach is based upon the premise that human interaction with automated systems embodies, in many significant respects, the reciprocal communicative and performance interchanges which characterize social interaction among humans. It propounds that behavior is a closed loop, selfregulated process mediated by motor feedback control (Smith and Smith, 1987, 1988). It further assumes that social interaction like all other biological activities is feedback controlled; similar to Powers (1973), except Smith and Smith pay more attention to the granular scales of sensorimotor performance. Figure 2, adapted from Smith and Smith (1988), portrays the general conception of the social cybernetics model.
Sensory Feedback
Sensor Sensory Feedback
Effector Sensory Feedback Control
Human-Computer Interaction
COMPUTER Output Feedback Control
Effector Feedback
Output Feedback
Output Device
Effector Feedback Control
PERSON
Sensory Feedback Control
Sensory Feedback
Receptor Sensory Feedback
Fig 2. Social cybernetic model of human-computer interaction (Smith & Smith, 1988; pp.701) 4.1 A Summary of the three cybernetic models The following properties and capabilities of LST are described in Miller (1978): 1.
2.
3.
4. 5.
It is a framework of basic concepts which is capable of organizing complex properties of It is a framework of basic concepts which is capable of organizing complex properties of systems in a rational and unified way. It can help to identify variables that have not been studied, as well as gaps in knowledge of systems at any given level. It can also reveal over or under-emphasis on certain adjustment processes. If research at one level establishes reliable relationships among variables or derives a useful indicator, similar measurements can be made at other levels in order to see whether they can enhance analysis at that level. Once a steady-state range has been determined at one level, its range may be estimated for a higher or a lower level. It can also aid in recognizing previously unseen relationships between a set of research findings at the same or different levels within different domains. Thus it facilitates the generalization of systems of all sorts.
In the perceptual control theory (PCT) developed by Powers (1973), some fundamental characteristics and assumptions are made consistent with physiological evidence (lowest levels) and experience (conscious perception). The nine levels proposed by Powers (1973, 1992) are as follows: 1. 2. 3. 4.
Intensity such as the magnitude of alarm in a nuclear power plant. Sensation or vector which consists of the first level experience attributed to the variables in the system. Configuration that stipulates the topology of information flow in the system. Transitions which address the changes of matter and energy from one level of perceptual experience (such as changes in display modality) to another.
5. 6.
7.
8.
9.
Sequence of behavioral primitives that are intentionally formed for specific acts rather than response to stimuli. Relationship that defines the strengths of relationships between effectors and sensors in the physiological apparatus controlling specific behaviors. Program which is a sort of automaton that regulates the effect of environment in control variables based on the system disturbance generated by the system. Principles which represent a set of interlocking theories and paradigms from behavioral psychology and system theory. For example, Powers (1973) alludes to the fact that what we perceive does not control us. It is our reference levels that originate within us that control our perception. This principle, for example, has direct link to Gibson’s (1979) ecological principle of direct perception. System concepts that describe concepts and abstractions in terms of a collective body of knowledge reviewed to show some commonality and reliance to the thematic theory. For example, Powers views perception-action continuum as a hierarchical organization of control systems in which each level of the hierarchy controls a different class of perception.
The social cybernetics approach (Smith and Smith, 1988) theorizes that group behaviors generate a closed-loop network of reciprocative, communicative and interactive acts. The control variables for such systems are controlled by mutual, dynamic, and adaptive exchange of many sensory feedback modalities. As noted by Smith and Smith (1988): “The field of behavioral cybernetics is founded upon the basic postulate that behavior is a closed-loop, self-regulated process mediated by motor feedback control” (pp. 692). As with LST and PCT, social cybernetics has both behavior and physiological variables mutually integrated. However, social cybernetics emphasizes the aspects of distributive mutual control and social interaction of the “energy-matter” transformation process. This is illustrated by the sensor-effector information exchange. Implicitly, out of these interactions, cooperation, behavioral coordination, and mutual understandings co-evolve. From the social cybernetics perspective, HCI may require the computer to have basic capacities for perception, action, and concept formation that can be shared with other computers (machines) and humans who interact with them. This requires the designers’ understanding of the semiotics of the cybernetic agents. Examples of these are described by Rasmussen (1986) as signs, signals, and symbols, or in the design rationale by a three-tier level of communication acts that involve syntactic, semantic, and/or pragmatic levels, respectively.
5. CONCLUSION The underlying purpose of this paper is to present LST as a cybernetic model for design and analysis of an adaptive HCI design. The primary tenet of discussion emphasizes that by drawing from the biologically inspired features of LST, user interface models can be developed to acquire adaptation. The advantage of this view is that it leads to a more flexible system with robust performance in a dynamic environment. This kind of HCI design system can anticipate the impact of a change in one information element or system level on the performance of the system. Defined in terms of a generic biological system, the LST framework can be used to recognize, in advance, the unanticipated events in the system. Thus, uncertain emergence system behaviors can be represented in HCI design. This will help to facilitate adaptive behaviours in HCI. Finally, the integration of soft-computing techniques like genetic algorithms, fuzzy logic and artificial neural networks into the LST-HCI paradigm for optimization, search and classification processes can ensure efficient selflearning and adaptability.
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