The importance of leaky levels for behavior-based AI

From the many possible perspectives in which an agent may be viewed, behavior-based AI selects observable actions as a particularly useful level of description. Yet behavior is clearly not structure, and anyone using behavior-based constraints to construct an agent still faces many implementational roadblocks. Such obstacles are typically avoided by adopting a finite state automaton (FSA) as a base representation. As a result, potential benefits from alternative formalisms are ignored. To explore these benefits, our work adopts a multilevel view of an agent: behaviors and FSAs are but two of many levels of description. We still focus on behaviors for the expression of design constraints, but we avoid using FSAs as an implementation. Our particular agent, Addam, is comprised of a set of connectionist networks, a substrate which promotes the automatic design of subsumptive systems. Moreover, the implementational choice has important behavioral consequences – some complex behaviors emerge due to interactions among networks and need not be specified explicitly. In this way, the underlying layers leak into one another, each affecting the others in subtle and desirable ways.