Behavioral Modeling and Simulation From Individuals to Societies (2008) / Chapter Skim
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5 Micro-Level Formal Models
5 Micro-Level Formal Models
Pages 149-214
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From page 149... ...
We then discuss expert systems, a legacy modeling approach that provides a framework for representing human expertise, and that now is often used as a programming paradigm in decision aiding systems. Finally we discuss decision theory and game theory and their limited applicability to individual, organizational, and societal modeling in general.
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Cognitive architectures are also referred to as agent architectures, computational cognitive models, and human behavior models. These simulation-based models aim to implement ndeed, I this report's focus on models and simulations that can contribute to some element of improving forecasting or explanation in a Department of Defense context may limit the ultimate utility of applying some of the models described herein (and elsewhere in the report) in a broader nonmilitary context.
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From page 151... ...
, typically, there is no direct learning resulting from the agent's interactions with the environment. However, the cognitive modeling community is beginning to recognize the limitations of human-constructed long-term memories in these models, and researchers are beginning to address the problem of automatic knowledge acquisition and learning in cognitive architectures (e.g., Anderson et al., 2003; Langley and Choi, 2006)
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Cognitive architectures thus contrast with the more narrowly scoped cognitive models (also referred to as micro models of cognition) , which Sensing and Cognition Perception • Multitasking • Attention • Memory and Learning Working Motor • Vision Memory • Situation Awareness Behavior • Hearing • Decision Making • Planning • Perception • Behavior Moderators Long-Term Memory Stimuli Goals / Tasks Responses World Model Maintain situation awareness Other Report important events declarative Assess threat to goals knowledge Assess alternatives Procedural Manage goals / tasks knowledge External world events 5-1.eps FIGURE 5-1 Example of a notional sequential cognitive architecture.
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From page 153... ...
and decision support contexts, with emphasis on training, decision aiding, interactive gaming, and virtual environments. Three recent reviews provide a comprehensive catalogue of a number of established or commercially available cognitive architectures: a report focusing on U.S.-developed systems (Andre, Klesen, Gebhard, Allen, and Rist, 2000, pp.
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The best sources for information regarding these architectures are conferences and workshops, such as the International Conference on Cognitive Modeling, the annual meeting of the Cognitive Science Society, symposia and conferences of the American Association for Artificial Intelligence, Autonomous Agents and Multi-Agent Systems, Human Factors, and BRIMS. See Table 2-1 for an overview of cognitive architectures used in military contexts.
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. Gradually, ACT-R evolved into a full-fledged cognitive architecture, with increasing emphasis on sensory and motor components and applications in military settings (e.g., modeling adversary behavior in military operations on urban terrain, MOUT, tactical action officers in submarines, radar operators on ships; Andre et al., 2000; Anderson et al., 2004)
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, to architectures capable of controlling autonomous agents. Soar represents the more extensively applied cognitive architecture and includes a number of training installations or exercises in which it has replaced human role players or autonomous air entities: TacAir-Soar at the Air Force Research Laboratory (AFRL)
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validation project, in which its performance was compared with other cognitive architectures and with human subjects in the context of air traffic control (Gluck and Pew, 2005)
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. It supports the construction of cognitive architecture from individual, independent "modules," each responsible for a particular cognitive (or perceptual)
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• Use of development environments to facilitate cognitive architec ture construction, which may include automatic KA/KE facilities, visualizations, and model performance assessment and analysis tools. • Increasing emphasis on empirical validation, frequently with respect to human performance data, and the development of validation methodologies and metrics (e.g., Gluck and Pew, 2005)
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The same report also emphasizes that a general validation of these complex models is not possible, and the models must be evaluated in the specific context for which they were developed. Within these constraints, several approaches exist for cognitive architecture validation, varying in the data requirements, time, and effort required and the quality of the validation results.
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To date, none of the existing cognitive architectures has been fully validated against generalized human performance. There are, however, a number of task-specific validation studies for many of the established architectures and a larger number of validation studies for single-process cognitive models (e.g., models of memory retrieval, visual attention models, GOMS-based models of user performance on specific tasks using a particular interface)
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Research architectures aim to develop a model of some aspect of human information processing, to enhance understanding of these phenomena by identifying the mediating structures and mechanisms. Specific applications of cognitive architectures include the control of autonomous synthetic agents and robots in a variety of settings, including operational systems in hostile or adverse environments, control of synthetic characters and agents in virtual reality environments, stand-ins for humans to enhance realism and believability in simulation-based training and assessment envi
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. The increasing emphasis on complex cognitive processes in military modeling is creating a broad range of applications for cognitive architectures modeling individual entities.
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As discussed above, the instantiation of an architecture in a new domain requires large amounts of human performance and task data, as well as information about the nature of internal problem solving and decision making. Whether obtained from empirical studies or from cognitive task analyses and knowledge elicitation interviews, the process of obtaining the necessary human data is highly labor-intensive and represents a major bottleneck in the development of cognitive architectures capable of emulating human problem solving, decision making, and performance.
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Computational models offer a higher degree of representational resolution for the internal processes than currently available human empirical data. In other words, while it is now possible to build detailed models of situation assessment, planning, learning, metacognition, and similarly complex cognitive processes, one cannot unequivocally identify the internal mechanisms and structures that mediate these functions in biological agents.
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, interchangeable plug-and-play components of generic architectures, and construction of cognitive architecture develop ment environments. • Facilitate architecture instantiation: via shared domain ontologies and human performance data repositories.
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• Explore new modeling formalisms: explore the applicability of addi tional representational and inferencing mechanisms to enhance cog nitive architecture performance, including nonsymbolic approaches such as chaos theory, and learning methods, such as genetic algorithms. • Models of groups and teams: apply cognitive architectures to mod els of groups and teams, in which the decision-making processes of the entity of interest can be sufficiently abstracted to enable the development of a cognitive architecture model representing the group as a whole.
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From page 168... ...
In addition to the objectives discussed for cognitive architectures, these models also serve to explore the nature of affective processes, the mechanisms of cognition-emotion interaction, and, in more applied contexts, to enhance the realism, believability, and effectiveness of synthetic agents and robots. Given the critical role of emotion in inter personal communication, these architectures are thus particularly relevant for organizational modeling (Hudlicka and Zacharias, 2005)
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provides a framework for integrating the multiple modalities of emotion, in the context of emotion generation. Emotions play a number of critical roles in biological agents, both intrapsychic and interpersonal.
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The specific constructs and processes represented in a particular cognitive-affective architecture depend on its objective, level of resolution, the specific processes modeled and their theoretical underpinnings, and any particular application, as well as the particular implementation approaches. Like their purely cognitive counterparts, cognitive-affective architectures typically include modules and functions that correspond to specific functions identified in biological agents, for example, emotion generation via cognitive appraisal, and generation of facial expressions.
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Applications and Benefits of Cognitive-Affective Architectures The applications and benefits of cognitive-affective architectures are similar to those of purely cognitive architectures, in both the theoretical and the applied realms. In addition, there are further categories of benefits, which follow from the primary roles of emotion in biological agents, as outlined above.
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5-3B.eps Part A illustrates the modules, data flow, and mental constructs that mediate emotion generation via cognitive appraisal and decision making. Part B illustrates how the effects of emotions, personality traits and other individual differences are translated into architecture parameters that control processing in the individual modules.
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Augmenting purely cognitive architectures and models with emotion also enables more accurate and realistic modeling of users in a variety of training and tutoring applications. Finally, since emotions play critical roles in biological agents, any computational model of biological information processing must necessarily take into consideration affective factors.
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Like their purely cognitive counterparts, cognitive-affective architectures can also be used for behavior prediction in a variety of settings, both individual and team, and across a range of contexts, ranging from simple task behavior prediction to adversary modeling for a variety of purposes, including counterterrorism. Since they include affective factors, which are considered to be key sources of human behavioral variability and an essential component of motivation, it can be argued that these models are superior to purely cognitive architectures regarding behavior prediction.
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For example, the Soar cognitive architecture has served as a framework for the implementation of several models of appraisal and emotion effects on behavior (e.g., Henninger, Jones, and Chown, 2003; Gratch and Marsella, 2004a)
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, all of which have been used to enhance the believability of synthetic agents. Recently, the appraisal theories of Scherer (Sander, Grandjean, and Scherer, 2005; Scherer et al., 2001)
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; and the robot Yuppy (Velásquez, 1999) , which uses emotion as a core component of the robot control system and integrates both cognitive and noncognitive triggers in the emotion generation process.
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While early models provided primarily domain-specific triggers and mapped these directly to specific emotions, more recent models interpose an intermediate step, whereby more abstract appraisal dimensions are first identified, such as relevance, novelty, unexpectedness, desirability, and ego involvement, which are then linked to specific emotions. Models of Emotion Effects on Cognition and Cognitive-Affective Interactions Architectures that focus on appraisal typically link the resulting emotion to specific behavioral results, most often to facial expressions, gestures, speech, or behavioral choices by the associated agents.
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. Several attempts have been made to model emotion effects on decision making in the context of decision-theoretic models, which need to be aug
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Cognitive-Affective Architectures Several cognitive-affective architectures have already been mentioned in the context of controlling agent or robot behavior and are described above in the context of either emotion generation or emotion effects on cognition and behavior (Velásquez, 1999; Breazeal and Brooks, 2005; Bach, 2007)
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Given the importance of emotion in motivation and behavior control, one can argue that any models attempting prediction must in fact include affective factors, while keeping in mind the general limitations of predictions of individual behavior already discussed. These applications include those outlined in Chapter 9: disrup
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As is the case with cognitive architectures, no existing emotion models or cognitive-affective architectures have been validated across multiple contexts or a broad range of metrics. However, some important evaluation and validation approaches and studies exist.
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As is the case with cognitive architectures, these validation studies are performed via a range of methods, including the weaker heuristic and qualitative evaluations and increasingly focusing on comparisons with human data. Examples of these efforts include evaluation of MAMID's parameter-based model of emotion effects, which used a heuristic evaluation approach to evaluate the model's ability to match human data at a qualitative level; establishing the validity of an augmented ACT-R architecture to model effects of stress on subtraction, using data from existing empirical studies (Ritter et al., 2002)
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. The very nature of emotion and affective processes as complex, m ultiple-modality phenomena makes modeling affective processes and c ognitive-affective architecture more challenging than modeling purely cognitive architectures.
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The objective of cognitive architectures is to emulate human perceptual and decision-making capabilities, frequently in the context of basic research aimed at advancing understanding of these processes, or to control the behavior of synthetic agents or robots. ES architectures are typically much simpler than cognitive architectures, the latter typically containing modules that correspond to functional components of the decision-making process (e.g., situation assessment, goal selection) or the mind (e.g., attention, long-term memory)
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• Knowledge acquisition capabilities to facilitate the acquisition (from existing technical materials) or the elicitation (from human experts)
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However, the use of ES shells is more common. Shells are development environments that facilitate ES development by providing system components and templates for structuring the necessary knowledge, thereby facilitating the knowledge engineering required to obtain the necessary knowledge from the expert(s)
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188 BEHAVIORAL MODELING AND SIMULATION State of the Art ESs represent one of the more successful applications of AI and are used extensively in multiple types of industrial and government applications in the United States and abroad, particularly in Asia. ESs have been applied to a range of problem types and across a broad range of domains.
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To help address this problem, a number of automatic knowledge engineering tools have been developed, some of which use established domain ontologies (Puerta et al., 1993) , and researchers are exploring the application of machine learning methods to the automatic development of knowledge bases from training cases.
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Relevance, Limitations, and Future Directions Relevance From the list of current applications of ESs above, it is clear that those dealing with human individual or social behavior could be useful in many ways. ESs might be used with knowledge bases comprising profiles of individuals (e.g., political or military leaders)
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Another limitation is the extensive effort required to build the necessary knowledge bases and to maintain consistency when the knowledge base is modified. Ideally, the developer or user could add, delete, or modify
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The main approaches addressing this problem are automatic knowledge engineering tools, shared ontologies, and standardized domain languages. Finally, one of the major limitations is the difficulty in deciding whether an ES-based system is the most appropriate solution to the problem at hand, given the costs and effort often required for ES development.
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Decision Theory and Game Theory Overview This section provides a brief overview of decision theory and game theory and their relevance to the individual and organizational modeling problem. In the earlier sections of this chapter we discussed many of the
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Nevertheless, game theory and decision theory can handle this type of uncertainty as we discuss below. Those outside economics and political science criticize the rational choice assumption, that is, the assumption of payoff maximizing behavior, on the grounds that it lacks descriptive accuracy.
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However, for much of the social, statistical, and computer sciences and for the network models and link analysis models discussed later in Chapter 6, the random behavior assumption is used as the baseline. We can distinguish between two types of models within the rational actor paradigm: decision theory models and game theory models.
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From page 196... ...
We further assume that the military commander can make accurate assessments of the number of lives lost by following each action conditional on each state and that those are shown in Table 5-1. This scenario illustrates why some consider decision theory a useful modeling tool and the reasons why decision theory ends up being not that useful in practice.
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Thus, the question is whether to supply or to negotiate with the village leader. An important caveat is that, were it possible to overcome the obstacles to applying decision theory, the commander would still need computational support to correctly apply a decision theory model.
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To model strategic adversaries, game theory is used, which we cover next. In our earlier discussion of cultural and cognitive models, we noted that people and cultures differ in how they respond in uncertain environments.
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In a game theory model, as in decision theory, one assumes that each actor has a payoff function. And the same caveats apply as with decision theory: it may be impossible to know this function or take too much time to determine it.
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When one thinks of a game, be it football or chess, one thinks of sequences of moves, of ebbs and flows. Game theory focuses instead on equilibria.
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Thus, a key difference between game theory models and nongame-theoretic agent-based models, which we discuss later, is what is assumed about behavior. Game theorists assume that behavior somehow gets the players to equilibrium, whereas, in most agent-based models, equilibrium is never reached and behavior is the result of various processes -- cognitive, social, political, cultural, and so on.
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Already we see the value in using game theory in that it forced us to define the targets and their relative payoffs. 11 omputational game theory does allow for more complex multiplayer games.
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Thus, 20 percent of the time, the terrorist group succeeds and the host country has no resources allocated to the target despite having a four to one advantage in resources and behaving optimally. Counterintuitive results like this are a hallmark of game theory models.
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In sum, game theoretic models can include cultural forces, but those forces must be well defined and analytically tractable. The movement to expand game theory by taking networks and culture into account is promising.
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Although recently game theorists have begun to study learning models, they tend to consider simple two-person games and not the more complex, multiplayer situations characteristic of the real world. Future Research and Development Requirements The potential for decision theory and game theory hinges on their a bility to capture the complexities of real people and the real world.
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In their approach, game theory becomes a preliminary tool: it defines the set of possible outcomes. Detailed historical and cultural knowledge from subject matter experts then selects from among those equilibria.
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. Behavioral game theory: Experiments in strategic interaction.
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. Game theory evolving: A problem-centered introduction to modeling stra tegic interaction.
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. Modeling emotion in symbolic cognitive architectures.
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Gray (Ed.) , Advances in cognitive models and cognitive architectures.
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. A review of computer-based human behavior representations and their relation to military simulations.
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. Steps towards including behavior moderators in human performance models in synthetic environments.
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In Proceedings of the Nineteenth National Conference on Artificial Intel ligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence. C ambridge, MA: AAAI Press/MIT Press.
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Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute.
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