Action Selection, Control State and Reactive, Behavior-Based AI

One contribution of my approach to behavior-based AI has been to separate the organization of control from the flow of data --- separating when a behavior should execute from how . When is determined by action selection, how by ordinary code organized in behavior libraries.

Within the flow of control, I have added mechanisms for focusing attention and maintaining decision state through time. This reduces the combinatorics of the problem of action selection and increases behavior coherence. On the other hand, it reduces reactiveness, since not all behaviors are actively expressing actions at any one time, and perception is a part of behavior. Hierarchy and Sequence vs. Full Parallelism in Reactive Action Selection Architectures, (from The Sixth International Conference on the Simulation of Adaptive Behavior (SAB2000)) demonstrates that this does not necessarily lead to a reduction of performance, even in highly dynamic environments.

I'm hardly the only person working in this area, see this page of related research for some of the others. What makes my approach special is:

• the retention of behavoir based modularity as powerful program entities, particularly in their application to assisting learning , and
• an interesting decomposition of control elements.