COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.
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| Title: | COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning. |
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| Language: | English |
| Authors: | Gratch, Jonathan, DeJong, Gerald, Illinois Univ., Urbana. Dept. of Computer Science. |
| Peer Reviewed: | N |
| Page Count: | 17 |
| Publication Date: | 1992 |
| Sponsoring Agency: | National Science Foundation, Washington, DC. |
| Document Type: | Information Analyses Reports - Research |
| Descriptors: | Algorithms, Artificial Intelligence, Comparative Analysis, Computer System Design, Learning Strategies, Planning, Probability, Problem Solving, Research Needs, Search Strategies, Statistical Analysis, Systems Development |
| Abstract: | In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system. COMPOSER embodies a probabilistic solution to the utility problem. It is implemented in the PRODIGY architecture. We compare COMPOSER to four other approaches which appear in the literature: (1) PRODIGY/EBL's Utility Analysis; (2) STATIC's Nonrecursive Hypothesis; (3) DYNAMIC: A Composite System; and (4) PALO's Chernoff Bounds. (Contains 24 references.) (Author/ALF) |
| Entry Date: | 1993 |
| Accession Number: | ED353955 |
| Database: | ERIC |
| Abstract: | In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system. COMPOSER embodies a probabilistic solution to the utility problem. It is implemented in the PRODIGY architecture. We compare COMPOSER to four other approaches which appear in the literature: (1) PRODIGY/EBL's Utility Analysis; (2) STATIC's Nonrecursive Hypothesis; (3) DYNAMIC: A Composite System; and (4) PALO's Chernoff Bounds. (Contains 24 references.) (Author/ALF) |
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