Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
2000
We propose a framework for robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGologmodel 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 officedelivery robotics domain.
Keywords:
- Inductive programming
- Artificial intelligence
- Programming paradigm
- Machine learning
- Markov decision process
- Programming domain
- Computer science
- Extensible programming
- Fifth-generation programming language
- Programming language theory
- Symbolic programming
- Declarative programming
- Functional reactive programming
- Functional logic programming
- Partially observable Markov decision process
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