Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to two-valued logic. Moreover, there are few if any published reports of expert systems in real-world use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems. 1

the paper presented fuzzy logics

Ultimate widespread use of CGF entities in tactical simulations will depend on how easy it will be to develop, refine and maintain models of the behaviors to be represented. However, proper vehicle behavior model development for CGF applications can be difficult as well as expensive. Means to quickly and effectively create models for new vehicles and/or behaviors must be developed to permit CGF models to be widely used in the future. One realistic approach to overcoming this model generation bottleneck is to create and refine vehicle models through automated observation of the behavior of an entity being controlled by a human expert in a simulation. This is a learning paradigm quite commonly used by humans. This paper describes an on-going research effort that introduces some new ideas on how to accomplish autonomous model development through observation.