Representing Human Behavior in Military Simulations Interim Report (1997) / Chapter Skim
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PROMOSING DEVELOPMENTS IN HUMAN BEHAVIOR RESEARCH
PROMOSING DEVELOPMENTS IN HUMAN BEHAVIOR RESEARCH
Pages 32-43
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From page 32... ...
The following sections provide a brief description of current human behavior modeling research efforts in learning and memory, attention and performance, decision making, situation awareness, and organizational structure. LEARNING AND MEMORY Learning, one of the oldest topics in psychology, has recently become one of the most intensively studied topics in cognitive science.
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From page 33... ...
For evolutionary models, genetic algorithms or combined neural network genetic algorithm procedures have been used. With respect to the military context, these learning models can be applied at different levels.
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From page 34... ...
Some advantages of neural networks are their robustness to noisy inputs, their ability to gradually deteriorate with damage (as opposed to the brittle behavior of rule-based systems) , and their ability to react intelligently to novel situations (provided that the situation has some similarity to previous experience)
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From page 35... ...
Since attention is the mechanism by which this capacity is allocated, whether one conceives of a single pool of capacity or multiple and relatively separate capacities, it is essential for accurate modeling that attentional processes be represented properly in human behavior representations. Without attentional constraints, the simulated combatant possesses superhuman capacities that would produce grossly unrealistic training and test environments for humans and overly optimistic results for perfor mance.
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DECISION MAKING The current military simulation models reviewed by the panel make very little use of sophisticated decision-making mechanisms. Decision making in these models is essentially reduced to checking preference values over a list of proposed actions selected for the current situation.
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An optimality model is a model in which the simulated agent is trying to locate the optimal solution for a problem. Game theory and operations resource optimizing functions are examples.
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From page 38... ...
could be used for simulating command-level decisions because they provide an optimal solution for planning strategies with competing agents. Decision scientists employ decision trees to represent decisions in a complex dynamic environment.
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For example, the information-processing approach used in MIDAS is based on a strictly serial processing assumption, which is known to be inconsistent with a large body of research on human cognition that emphasizes the importance of parallel distributed processing for fast but accurate cognitive performance. Progress has also been made on the development of dynamic decision models that are highly relevant to the goals of the Defense Modeling and Simulation Office (Busemeyer and Townsend, 1993; Townsend and Busemeyer, 1995; Grossberg and Gutowski, 1987~.
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From page 40... ...
Thus, adaptive learning models and dynamic decision models can be synthesized into a general model of learning and motivation for real-time adaptive decision making. SITUATION AWARENESS Situation awareness is defined as the individual's state of knowledge or mental model of the surrounding situation or environment.
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From page 41... ...
There currently exists no descriptive model that has been developed into a computational model, for actual emulation of pilot decision-making behavior in real-time simulation studies. In contrast to the status of descriptive models, few prescriptive models of situation awareness have been proposed or developed.
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From page 42... ...
Most of the computational modeling work on unit-level models employs the use of multiagent models. These models range from the more symbolic distributed artificial intelligence models to models using one of the various complex adaptive agent techniques, such as genetic algorithms (Holland et al., 1986; Macy, 1991a, b; Crowston, 1994, in press)
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From page 43... ...
None of the computational unit-level models is at the point of plugging directly into a current Defense Modeling and Simulation Office platform as the model of the unit. In part this is because existing unit models have either too limited a repertoire of command and control structures or too limited a model of task.
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