Abstract
Behavior based AI [8,18] has questioned the need for modeling intelligent agency using generalized cognitive modules for perception and behavior generation. Behavior based AI has demonstrated successful interactions in unpredictable environments in the mobile robot domain [7, 8]. This has created a gulf between traditional approaches to modeling intelligent agency and behavior based approaches. We present an architecture for intelligent autonomous agents which we call GLAIR (Grounded Layered Architecture with Integrated Reasoning) [13, 14, 12]. GLAIR is a general multi-level architecture for autonomous cognitive agents with integrated sensory and motor capabilities. GLAIR offers an unconscious layer for modeling tasks that exhibit a close affinity between sensing and acting, i.e., behavior based AI modules, and a conscious layer for modeling tasks that exhibit delays between sensing and acting. GLAIR provides learning mechanisms that allow for autonomous agents to learn emergent behaviors and add it to their repertoire of behaviors. In this paper we will describe the principles of GLAIR and systems we have developed that demonstrate how GLAIR based agents acquire and exhibit a repertoire of behaviors at different cognitive levels.
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