Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40(1-3):63--118, 1989. Home/Search Document Not in Database Summary Related Articles Check |
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Approximate Policy Iteration with a Policy Language Bias - Fern, Yoon, Givan (2003) (1 citation) (Correct)
....but for the slow prototype Scheme implementation. 5 Related Work Typically, previous learning for planning systems [22] learn from small problem solutions to improve the efficiency and or quality of planning. Two primary approaches are to learn control knowledge for search based planners, e.g. [23, 27, 10, 15, 1], and, more closely related, to learn stand alone control policies [17, 19, 28] The former work is severely limited by the utility problem (see [21] i.e. being swamped by low utility rules. Critically, our policy language bias confronts this issue by preferring simpler policies. Regarding ....
S. Minton, J. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanationbased learning: A problem solving perspective. AIJ, 40:63--118, 1989.
A Framework for Programming Embedded Systems: Initial Design and.. - Thrun (1998) (1 citation) (Correct)
....78] and inductive logic programming [73, 85] has led to a variety of learning algorithms that modify programs written in first order logic based on examples. Several research teams have integrated such learning algorithms into problem solving architectures, such as SOAR [89, 26, 66, 57] PRODIGY [67, 39] and THEO [70] These architectures all require declarative theories of the domain, using built in theorem provers or special purpose planners to generate control. Learning is applied to modify the domain theory in response to unexplained observations, or to speed up the reasoning process. In some ....
S. Minton, J. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40:63--118, 1989.
Learning Analytically and Inductively - Mitchell, Thrun (1995) (2 citations) (Correct)
....conjecture that humans learn through such explanations (see, for example, Chi and Bassok, 1989] Qin et al. 1992] Ahn and Brewer, 1993] Analytical learning methods have been used successfully in a number of applications notably for learning rules to control search. For example, Prodigy [Minton et al. 1989] is a domain independent framework for means ends planning that uses explanation based learning to acquire search control knowledge. Prodigy learns general rules that characterize concepts such as situations in which pursuing subgoal x will lead to backtracking. Given a specific problem solving ....
....problem solving domain defined by a set of states, operators, and goals, Prodigy learns control rules that significantly reduce backtracking when solving problems in this domain. It has been demonstrated to learn search control rules comparable to hand coded rules in a variety of task domains [Minton et al. 1989]. The chunking mechanism in Soar [Laird et al. 1986] also provides an example of ana lytical learning, as explained in [Rosenbloom and Laird, 1986] In Soar, problem solving corresponds to search in problem spaces (a problem space is defined by problem states and operators) Whenever Soar has ....
Steve Minton, Jaime Carbonell, Craig A. Knoblock, Dan R. Kuoka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspec- tive. Artificial Intelligence, 40:63-118, 1989.
Learning Analytically and Inductively - Mitchell, Thrun (1995) (2 citations) (Correct)
....conjecture that humans learn through such explanations (see, for example, Chi and Bassok, 1989] Qin et al. 1992] Ahn and Brewer, 1993] Analytical learning methods have been used successfully in a number of applications notably for learning rules to control search. For example, Prodigy [Minton et al. 1989] is a domain independent framework for means ends planning that uses explanation based learning to acquire search control knowledge. Prodigy learns general rules that characterize concepts such as situations in which pursuing subgoal x will lead to backtracking. Given 3 a specific problem ....
....problem solving domain defined by a set of states, operators, and goals, Prodigy learns control rules that significantly reduce backtracking when solving problems in this domain. It has been demonstrated to learn search control rules comparable to hand coded rules in a variety of task domains [Minton et al. 1989]. The chunking mechanism in Soar [Laird et al. 1986] also provides an example of analytical learning, as explained in [Rosenbloom and Laird, 1986] In Soar, problem solving corresponds to search in problem spaces (a problem space is defined by problem states and operators) Whenever Soar has no ....
Steve Minton, Jaime Carbonnel, Craig A. Knoblock, Dan R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40:63-118, 1989.
Learning Analytically and Inductively - Mitchell, Thrun (1995) (2 citations) (Correct)
....conjecture that humans learn through such explanations (see, for example, Chi and Bassok, 1989] Qin et al. 1992] Ahn and Brewer, 1993] Analytical learning methods have been used successfully in a number of applications notably for learning rules to control search. For example, Prodigy [Minton et al. 1989] is a domain independent framework for means ends planning that uses explanation based learning to acquire search control knowledge. Prodigy learns general rules that characterize concepts such as situations in which pursuing subgoal x will lead to backtracking. Given a specific problem solving ....
....problem solving domain defined by a set of states, operators, and goals, Prodigy learns control rules that significantly reduce backtracking when solving problems in this domain. It has been demonstrated to learn search control rules comparable to hand coded rules in a variety of task domains [Minton et al. 1989]. The chunking mechanism in Soar [Laird et al. 1986] also provides an example of analytical learning, as explained in [Rosenbloom and Laird, 1986] In Soar, problem solving corresponds to search in problem spaces (a problem space is defined by problem states and operators) Whenever Soar has no ....
Steve Minton, Jaime Carbonell, Craig A. Knoblock, Dan R. Kuoka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40:63-118, 1989.
A Uniform Computational Model for Natural Language Parsing and.. - Neumann (1994) (7 citations) (Correct)
....knowledge. As a method, EBL performs four different learning tasks: generalization, chunking, oper ationalization and justified analogy JEllman, 1989] Typically, the purpose of EBL is to produce a description of a concept that enables instances of that concept to be recognized eificiently [Minton et al. 1989]. More fundamentally, EBL is a method for improving problem solving performance through experience. From this perspective, EBL is also of cognitive relevance, since it can be used to explain why humans tend to phrase ideas in the same way most of the time by adapting to a collection of idioms or ....
S. Minton, J. G. Carbonell, C. A. Knoblock, D. R.Kuokka, O. Etzioni, and Y.Gil. Explanation-based learning: A problem solving perspective. Artifical Intelligence, 40:63-118, 1989.
A Bayesian Approach to Tackling Hard Computational Problems - Horvitz, Ruan, Gomes (2001) (17 citations) (Correct)
....paper. 6 Related Work Learning methods have been employed in previous research in a attempt to enhance the performance optimize reasoning systems. In work on speed up learning, investigators have attempted to increase planning eciency by learning goal speci c preferences for plan operators [22, 19]. Khardon and Roth explored the o ine reformulation of representations based on experiences with problem solving in an environment to enhance run time eciency [18] Our work on using probabilistic models to learn about algorithmic performance and to guide problem solving is most closely related to ....
S. Minton, J. G. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanation-based learning: A problem solving perspective. Articial Intelligence, 40:63-118, 1989.
Principles of Efficient Inference - Kautz (2001) (Correct)
....we believe it has the potential for breakthrough impact on the field. 2. 4 Understanding Clause Learning A different approach to applying machine learning to efficient inference grows out of the area of speed up learning , which roughly amounts to speeding inference by caching useful lemmas [53, 67]. In the context of SAT this technique is referred to as clause learning . In clause learning each non solution leaf in the tree is analyzed to determine which particular choices led to the contradiction; the negation of this choice set is then added to the formula as a new clause. Clause ....
S. Minton, J. G. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanationbased learning: A problem solving perspective. Artificial Intelligence, 40:63--118, 1989.
A Bayesian Approach to Tackling Hard Computational Problems - Horvitz, Ruan, Gomes (2001) (17 citations) (Correct)
....paper. 7 Related Work Learning methods have been employed in previous research in a attempt to enhance the performance optimize reasoning systems. In work on speed up learning, investigators have attempted to increase planning eciency by learning goal speci c preferences for plan operators [22, 19]. Khardon and Roth explored the o ine reformulation of representations based on experiences with problem solving in an environment to enhance run time eciency [18] Our work on using probabilistic models to learn about algorithmic performance and to guide problem solving is most closely related to ....
S. Minton, J. G. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanation-based learning: A problem solving perspective. Articial Intelligence, 40:63-118, 1989.
A Perspective View And Survey Of Meta-Learning - Vilalta, Drissi (2002) (7 citations) (Correct)
....is able to provide explanations of its reasoning and justi cations of its solutions. Meta learning has been used in analytic learning for constraintsatisfaction problems (Minton, 1993) Analytic learning (e.g. explanation based learning, derivational analogy) exploits problem solving experience (Minton, 1989). When applied at a meta learning level, the idea is to use meta level theories to help the system reason about the problem solver s base level theory. A meta level analysis is appropriate when the base level theory is intractable (Minton, 1993) Meta learning can also be applied to areas like ....
Minton Steve (1989). Explanation Based-Learning: A problem Solving Perspective. Articial Intelligence, 40, 63-118.
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Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40(1-3):63--118, 1989.
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Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40(1-3):63--118, 1989.
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Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40(1-3):63--118, 1989.
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S. Minton, J. Carbonell, C. Knoblock, D. Kuokka, O. Etzioni, and Y. Gil, "Explanation-Based Learning: A Problem Solving Perspective," Artificial Intelligence, vol. 40, pp. 63--118, 1989.
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Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Dan R. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation-based learning: A problem-solving perspective. Artificial Intelligence, 40(1--3):63--118, 1989.
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S Minton, J G Carbonell, C A Knoblock, D R Kuokka, O Etzioni, and Y Gil. Explanation-based learning: A problem-solving perspective. Artificial Intelligence, 40:63--118, 1989.
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S. Minton, J.G Carbonell, C.A. Knoblock, D.R. Kuokka, O. Etzioni, and Y. Gil. Explanation-based learning: A problem solving perspective. Artificial Intelligence, 40:63--118, 1989.
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Steven Minton, Jaime Carbonell, Craig Knoblock, D. Kuokka, Oren Etzioni, and Yolanda Gil. Explanation based learning: A problem solving perspective. Artificial Intelligence, 40:63--118, 1989.
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S. Minton, J. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil. Explanationbased learning: A problem solving perspective. AIJ, 40:63--118, 1989.
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S. Minton, J.G Carbonell, C.A. Knoblock, D.R. Kuokka, O. Etzioni and Y. Gil. Explanation-Based Learning: A Problem Solving Perspective. Artificial Intelligence, 40:63-- 118, 1989.
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S. Minton, J.G. Carbonell, C.A. Knoblock, D.R. Kuokka, O. Etzioni andY. Gil. Explanation-BasedLearning: A Problem Solving Perspective. Artificial Intelligence, 40:63--118, 1989.
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