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Decision-Theoretic, High-Level Agent Programming in the Situation Calculus

Craig Boutilier, Raymond Reiter, Mikhail Soutchanski, Sebastian Thrun

Year
2000
Citations
195

Abstract

We proposea framework for robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGolog model allows one to partially specify a control program in a highlevel, logical language, and provides an interpreter that, given a logical axiomatization of a domain, will determine the optimal completion of that program (viewed as a Markov decision process). We demonstrate the utility of this model with results obtained in an office delivery robotics domain. 1 Introduction The construction of autonomous agents, such as mobile robots or software agents, is paramount in artificial intelligence, with considerable research devoted to methods that will ease the burden of designing controllers for such agents. There are two main ways in which the conceptual complexity of devising controllers can be managed. The first is to provide languages with which a programmer can specify a control program with relative eas...

Keywords

Markov decision processComputer scienceSituation calculusInterpreterDomain (mathematical analysis)Programming languageProgramming domainSymbolic programmingArtificial intelligenceInductive programming

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