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A HYBRID AGENT

ARCHITECTURE FOR REACTIVE

SEQUENTIAL DECISION MAKING

Ron Sun

Todd Peterson

Department of Computer Science
The University of Alabama

1 INTRODUCTION

How does an autonomous agent that interacts with an environment learn to survive in the environment and make the most out of it? More specifically, how can it develop a set of coping skills that are highly specific (geared toward very particular situations) and thus highly efficient but, at the same time, acquire sufficiently general knowledge that can be readily applied to a variety of different situations? Although humans seem to possess such abilities and seem to be able to achieve an appropriate balance between the two sides, existing AI systems fall far short.

There has been a great deal of work demonstrating the difference between procedural knowledge and declarative knowledge (or conceptual and subconceptual knowledge; e.g., Anderson 1982, 1990, Keil 1989, Damasio et al. 1990, Sun 1994). It is believed that a balance of the two is essential to the development of complex cognitive agents. For example, one way to learn a sequential decision task, such as navigating a maze (see Figure 1 for an example), is through trial and error: repeated practice gradually gives rise to a set of procedural skills that deal specifically with the practiced situations and their minor variations. However, such skills may not be transferable to truly novel situations, since they are so embedded in specific contexts and tangled together. In order to deal with novel situations, the agent needs to discover some general rules. Generic knowledge helps to guide the exploration of novel situations, and reduces the time (i.e., the number of trials) necessary to develop specific skills in new situations. Generic knowledge can also help in communicating the process and the skill of navigation to other agents. If properly used, generic knowledge

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