Paper Info
Title
Robot action planning via explanation-based learning
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
Year
DOI
Venue
2000
10.1109/3468.833104
IEEE Transactions on Systems, Man, and Cybernetics, Part A
Keywords
Field
DocType
problem formulation,example study,robot action planning,computer-realized recognition,explanation-based learning,strips,learning artificial intelligence,feedback,control systems,robots
Robot learning,Explanation-based learning,Computer science,Hyper-heuristic,Heuristics,Artificial intelligence,Action planning,Robot,Machine learning
Journal
Volume
Issue
ISSN
30
2
1083-4427
Citations 
PageRank 
References 
2
0.38
13
Authors
1
Name
Order
Citations
PageRank
H. Tianfield1111.61