A comparison of computational cognitive models : agent-based systems versus rule-based architectures

Abstract

Increased operational costs and reductions in force size are two of the major factors driving the need for improved computer simulations within the military community. Human performance models are used in various aspects of simulation, including controlling computer generated forces, tactical decision aides, intelligent tutoring systems and new system design. This research makes a comparison between two categories of human performance models, multi-agent systems and rule-based architectures. Each type of model has its own strengths and weaknesses, and is therefore better suited for certain applications. Complex military simulations need human performance models that take advantage of the strengths of more than one type of model. The purpose of this research is to compare the implementation and performance of these two models, and to demonstrate the need for hybrid systems that employ the best aspects of models for a given situation.