Journal article
A high-performance explanation-based learning algorithm
Artificial intelligence, Vol.69(1), pp.1-50
1994
DOI: 10.1016/0004-3702(94)90077-9
Abstract
The main contribution of this paper is a new domain-independent explanation-based learning (EBL) algorithm. The new EBL∗DI algorithm significantly outperforms traditional EBL algorithms both by learning in situations where traditional algorithms cannot learn as well as by providing greater problem-solving performance improvement in general. The superiority of the EBL∗DI algorithm is demonstrated with experiments in three different application domains. The EBL∗DI algorithm is developed using a novel formal framework in which traditional EBL techniques are reconstructed as the structured application of three explanation-transformation operators. We extend this basic framework by introducing two additional operators that, when combined with the first three operators, allow us to prove a completeness result: in the formal framework, every EBL algorithm is equivalent to the application of the five transformation operators according to some control strategy. The EBL∗DI algorithm employs all five proof-transformation operators guided by five domain-independent control heuristics.
Details
- Title: Subtitle
- A high-performance explanation-based learning algorithm
- Creators
- Alberto Segre - Cornell UniversityCharles Elkan - University of California San Diego
- Resource Type
- Journal article
- Publication Details
- Artificial intelligence, Vol.69(1), pp.1-50
- DOI
- 10.1016/0004-3702(94)90077-9
- ISSN
- 0004-3702
- eISSN
- 1872-7921
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 1994
- Academic Unit
- Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science
- Record Identifier
- 9984259497302771
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