***** Submitted to Ijcai-95 ***** Action Model Learning and Action Execution in a Reactive Agent
نویسنده
چکیده
We present a reactive agent that successfully learns action models in a continuous and dynamic environment. The TRAIL agent uses teleo-reactive trees to integrate planning, reactive execution, and performance improvement through action model learning. This paper discusses the diiculties of action model learning in the face of irrelevant features, durative actions, and stochastic action eeects, and presents TRAIL's solutions to these problems. We focus on two particular aspects of TRAIL: the identiication of action successes and failures during the execution of a teleo-reactive tree, and the analysis of execution failures through the use of experimentation to distinguish among possible causes.
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