A Connectionist-Symbolic Approach To Modeling Agent Behavior: Neural Networks Grouped By Contexts


A recent report by the National Research Council (NRC) declares neural networks “hold the most promise for providing powerful learning models”. While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks’ architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks’ performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work.

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)



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Article; Proceedings Paper

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84942883649 (Scopus)

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