Modeling Human and Organizational Behavior Application to Military Simulations (1998) / Chapter Skim
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5 Memory and Learning
5 Memory and Learning
Pages 129-149
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From page 129... ...
. A great portion of this research is directly or potentially relevant to the representation of human behavior in military simulations, but even a survey of the field would require several long books.
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Short-term memories allow control processes to be carried out because they hold information for temporary periods of, say, up to a minute or more. The long-term memory system can be divided by function or other properties into separate systems, but this is a matter of current research.
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Although modeling the course of learning over 10 years of intensive practice may be useful for military simulations only in rare instances, other sorts of learning over shorter time spans will often be important. To carry this discussion further, it is necessary to discuss the different types of memory and their modeling.
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From page 132... ...
Although the existence of implicit memory effects is well established, the importance of including them in military models and simulations is not yet clear. Therefore, the focus here is on episodic and generic storage and retrieval, both of which are essential components of any simulation of human behavior.
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For episodic storage and retrieval, the best current models assume separate storage of events (e.g., the search of associative memory [SAM] model of Raaijmakers and Shiffrin, 1981; the ACT-R model of Anderson, 1993; the retrieving effectively from memory [REM]
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A second problem just now surfacing in the field is the nature of retrieval from generic memory: To what degree are generic retrieval processes similar to those used in explicit retrieval (i.e., retrieval from episodic memory)
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Because accurate and rapid access to general and military knowledge, as opposed to access to recent events, is of great importance in producing effective action and in modeling the cognition of participants in military situations, and because this area of modeling is not yet well developed, the military may eventually need to allocate additional resources to this area of work. MODELING OF HUMAN LEARNING Two types of learning are of potential importance for military simulations.
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Neural network models are reviewed last; although they hold the most promise for providing powerful learning models, they are also the most difficult to integrate into current military simulations. Research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based technology.
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From page 137... ...
Again, chunking can, in principle, continually refine and adjust preference knowledge, but this capability must be shown to work in practice for large military simulations and rapidly changing environments. A second potential problem is that the conditions forming the antecedent of a production rule must be matched exactly before the production will fire.
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The retrieval process also includes learning mechanisms that change the associative strengths based on experience. One drawback of ACT-R is that it is a relatively complex system involving numerous detailed assumptions that are important for its successful operation.
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But using this approach would require precise knowledge of the joint probability distributions over the competing hypotheses, which is unrealistic for many military scenarios. Usually the decision maker does not have this information available, and instead must base the decision on experience with previous situations that are similar to the pres ent case.
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First, it is possible to include exemplar learning processes into the current rule-based architectures by modifying the principles for activation and selection of rules. The gain achieved by including this simple but effective learning process would offset the cost of making such a major modification.
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Moreover, several direct comparisons of neural network learning models with exemplar-based learning models have shown that the former models provide a more accurate account of the details of human learning than do exemplar models (Gluck and Bower, 1988; Nosofsky and Kruschke,1992~. Because neural networks provide robust statistical learning in noisy, uncertain, and dynamically changing environments, these models hold great promise for future technological developments in learning theory (see Haykin, 1994, for a comprehensive introduction)
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Neural networks offer great potential and power for developing new learning models for use in computer-generated agents. However, these models are also the most difficult to integrate into existing military simulations.
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It led to an evolving series of real-time neural network models that perform unsupervised and supervised category learning and classification, pattern recognition, and prediction: ART 1 (Carpenter and Grossberg, 1987a) for binary input patterns, ART 2 (Carpenter and Grossberg, 1987b)
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FIGURE 5.1 A typical unsupervised adaptive resonance theory neural network.
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Such a category represents all the F1 inputs I that send maximal input to the corresponding F2 node. These rules constitute a selforganizing feature map (Grossberg, 1972, 1976; Kohonen, 1989; von der Malsburg, 1973)
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Vigilance weighs how close input I must be to prototype V for resonance to occur. Varying vigilance across learning trials lets the system learn recognition categories of widely differing generalization, or morphological variability.
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Many such rules may coexist without mutual interference in ARTMAP, in contrast with many other learning models, such as back propagation. Other Learning Models Beyond neural networks, there are several other more complex forms of learning that may prove useful.
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Neural networks may offer the greatest potential and power for the development of robust learning models, but their integration into current simulation models will require much more time and effort. One way to facilitate this integration is to develop hybrid models that make use of rule-based problem-solving processes, exemplar representations, and neural network learning mechanisms.
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· Validate these models against real-world data. · Begin to explore the effects of learning, memory, and retrieval processes on group behavior.
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