Many learning systems suffer from the utility problem; that is, that time after learning is greater than time before learning. Discovering how to assure that learned knowledge will in fact speed up system performance has been a focus of research in explanation-based learning (EBL). One way to analyze the utility problem is by examining the differences between the match process (match search) of the learned rule and the problem-solving process from which it is learned. Prior work along these lines examined one such difference. It showed that if the search-control knowledge used during problem solving is not maintained in the match process for learned rules, then learning can engender a slowdown; but that this slowdown could be eliminated if the match is constrained by the original search-control knowledge. This article examines a second difference--- when the structure of the problem solving differs from the structure of the match process for the learned rules, time after learning can be greater than time before learning. This article also shows that this slowdown can be eliminated by making the learning mechanism sensitive to the problem-solving structure; i.e., by reflecting such structure in the match of the learned rule.
|
540
|
Soar: An architecture for general intelligence
– Laird, Newell, et al.
- 1987
|
|
471
|
Explanationbased generalization: a unifying view
– Mitchell, Keller, et al.
- 1986
|
|
380
|
Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem
– Forgy
- 1982
|
|
331
|
Explanation-based learning: an alternative view
– Dejong, Mooney
- 1986
|
|
279
|
Quantitative results concerning the utility of explanation-based learning
– Minton
- 1988
|
|
230
|
Chunking in Soar: The anatomy of a general learning mechanism
– Laird, Rosenbloom, et al.
- 1986
|
|
82
|
TREAT: A better match algorithm for AI production systems
– MIRANKER
- 1987
|
|
65
|
COMPOSER: a probabilistic solution to the utility problem in speedup learning
– Gratch, DeJong
- 1992
|
|
62
|
A Preliminary Analysis of the Soar Architecture as a Basis for General Intelligence
– Rosenbloom
- 1991
|
|
47
|
A Statistical Approach to Solving the EBL Utility Problem
– Greiner, Jurisica
- 1992
|
|
45
|
Explanation-based generalization: A unifying view. Machine Learning 1(1):47{80
– Mitchell, Keller, et al.
- 1986
|
|
38
|
Information filtering: Selection mechanisms in learning systems
– Markovitch, Scott
- 1993
|
|
33
|
PROLEARN: towards a prolog interpreter that learns
– Prieditis, Mostow
- 1987
|
|
32
|
Why Prodigy/EBL Works
– GRATCH, Etzioni, et al.
- 1990
|
|
31
|
Matching 100,000 learned rules
– Doorenbos
- 1993
|
|
27
|
Acquiring recursive and iterative concepts with explanation-based learning
– Shavlik
- 1990
|
|
21
|
Explanation-based learning: An alternative view. Machine Learning 1(1):145{176
– DeJong, Mooney
- 1986
|
|
17
|
The utility of EBL in recursive domain theories
– Subramanian, Feldman
- 1990
|
|
16
|
Match algorithms for generalized Rete Networks
– Lee, Schor
- 1992
|
|
15
|
Learning 10,000 chunks: What's it like out there
– Doorenbos, Tambe, et al.
- 1992
|
|
15
|
Comparison of the Rete and Treat production matchers for Soar (A summary
– Nayak, Gupta, et al.
- 1988
|
|
11
|
Efficient matching algorithms for the SOAR/OPS5 production system
– Scales
- 1986
|
|
11
|
Soar/PSM-E: Investigating match parallelism in a learning production system
– Tambe, Kalp, et al.
- 1988
|
|
11
|
Eliminating combinatorics from production match
– Tambe
- 1991
|
|
6
|
Empirical and analytical performance of iterative operators
– Shell, Carbonell
- 1991
|
|
5
|
Optimization of discrimination networks for active databases
– Hasan
- 1993
|
|
5
|
Constraining learning with search control
– Kim, Rosenbloom
- 1993
|
|
3
|
Soar6 release notes
– Doorenbos
- 1992
|
|
3
|
Constraining learning with search control
– Kim, Rosenbloom
- 1993
|
|
1
|
COMPOSER: A probabilisticsolution to the utilityproblem in speed-up learning
– Gratch, Dejong
- 1992
|
|
1
|
Transformation analyses of learning in Soar
– Kim, Rosenbloom
- 1995
|
|
1
|
A domain independent explanaion-based generalization
– Mooney, Bennett
- 1986
|
|
1
|
Uni-Rete: Specializing the Rete match algorithm for the unique-attribute representation
– Tambe, Kalp, et al.
- 1991
|
|
1
|
Mapping explanation-based learning onto Soar: The sequel. Technical Report :Transformation analyses of learning
– Kim, Rosenbloom
- 1995
|
|
