Robot action planning via explanation-based learning

Authors:
H. Tianfield
Affiliations:
Tongji Univ., Shanghai
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2000

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Abstract

Domain-specific searching heuristics is greatly influential upon the searching efficiency of robot action planning (RAP), but its computer-realized recognition and acquisition, i.e., learning, is difficult. This paper makes an exploration into this challenge. First, a problem formulation of RAP is made. Then, by applying explanation-based learning, which is currently the only approach to acquiring domain-specific searching heuristics, a new learning based method is developed for RAP, named robot action planning via explanation-based learning (RAPEL). Finally, an example study demonstrates the effectiveness of RAPEL