Steven Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988. 194  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in which knowledge gaps are identified, the reasons why particular hypotheses are generated, the strategies chosen for verifying candidate hypotheses, and the basis for choosing particular reasoning methods for each of these. Trace Meta XPs are similar to reasoning traces [Carbonell, 1986; Minton, 1988; Veloso Carbonell, 1993] or justification structures [Collins, Birnbaum, Krulwich, Freed , 1993; deKleer, Doyle, Steele, Sussman , 1977; Doyle, 1979] with the difference that Trace Meta XPs represent, in addition to the subgoal structure of the problem and justifications for operator ....

S. Minton. Learning effective search control knowledge: An explanation-based approach. Ph.D. thesis, Carnegie-Mellon University, Computer Science Department, Pittsburgh, PA, 1988. Technical Report CMU-CS-88-133.

....actually improve performance according to any realistic metric is, at best, inconclusive. For example, in [ Tambe and Newell, 1988 ] it is shown that chunking in SOAR can either degrade or improve performance, depending on the domain; and independent analyses in [ Shavlik, 1987 ] and [ Minton, 1988a ] reach contradictory conclusions about the asymptotic behavior of explanation based learning in improving performance of planners in the blocks world domain. In fact, it is hardly surprising that experimental evidence has failed to completely settle this issue, given the time requirements ....

....investigated, the questions when is SLL learning useful and even is SLL useful will not have been answered. In closing, we would like to make an attempt to dispell one possible source of confusion. Readers familiar with chunking and EBL techniques as used in problem solvers like Prodigy [ Minton, 1988a ] or SOAR [ Laird et al. 1986 ] may be aware that in these systems, chunking serves mostly to improve the control decisions made by the problem solver in particular to reorder goals and resolve goal conflicts. It may appear at first that the techniques in this paper are quite different; in ....

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Steven Minton. Learning effective search control knowledge: An explanation-based approach. Technical report, Carnegie-Mellon University Department of Computer Science, 1988.

....formalize the reasons that this is so. What s wrong with Strawman 1 Simply put, Strawman 1 s behavior is unsatisfying because its outputs are too general. In most EBL contexts, there is a penalty for overgeneralizing an example. If the learned rules are used to speed up problem solving, as in [Minton, 1988], then they will be overgeneral and (in most cases) needlessly inefficient; if the learned One possible choice for R is a recursive theory, as in [ Shavlik, 1990 ] rules are used to construct a new theory that better models the data, as in [Cohen, 1990b] then they will be inaccurate. How ....

Steven Minton. Learning effective search control knowledge: An explanationbased approach. Technical report, Carnegie-Mellon University Department of Computer Science, 1988.

....i.e. to automate the acquisition of knowledge that guides the problem solving search process. One approach to learning control knowledge consists of generating explanations for the local decisions made during the search process (DeJong and Mooney, 1986, Laird et al. 1986, Mitchell et al. 1986, Minton, 1988, Prez and Etzioni, 1992, Katukam and Kambhampati, 1994) These explanations become control rules that are used in future situations to prune the search space. These deductive approaches invest a substantial explanation effort to produce proven correct and complete control rules from a single (or ....

....of the search tree; Credit assignment; and Generation of control rules. The Bounded Explanation module behaves lazily in two aspects: It does not require to learn initially correct or complete control knowledge. In contrast with other eager approaches for learning control knowledge for planning (Minton, 1988, Etzioni, 1993) HAMLET does not require the learned knowledge to be correct initially. The incremental refinement will be responsible for the correctness of the control knowledge at the end of the learning process. Therefore, there is no need for additional domain axioms. It does not require to ....

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Steven Minton. Learning Effective Search Control Knowledge: An Explanation- Based Approach. Kluwer Academic Publishers, Boston, MA, 1988.

....Prodigy I Introduction Prodigy is an integrated planning and learning system. The system includes not only a planning algorithm but also procedures for learning and case based reasoning, which greatly increase the efficiency of the planner. For example, Prodigy is able to learn control rules [Minton, 1988], conduct experiments to acquire new knowledge [Gil, 1992] generate abstraction hierarchies [Knoblock, 1993] and use andlogical reasoning to recognize and exploit similarities between planning problems [Veloso, 1992] Prodigy s core, the planning algorithm itself, has been improved over the ....

Steven Minton. Learning effective search control knowledge: an explanation- based approach. Kluwer Academic Publishers, Boston, MA, 1988. 10

....Anderson and Farley, 1990] In PRODIGY, there is a clear division between the declarative domain knowledge (operators and inference rules) and the more procedural control knowledge. This simplifies both the initial specification of a domain and the incremental learning of the control knowledge [Minton, 1988, Veloso and Carbonell, 1990] Previous work in the linear planner of PRODIGY used explanation based learning techniques [Minton, 1988] to extract from a problem solving trace the explanation chain responsible for a success or failure and compile search control rules. We are now extending this ....

....rules) and the more procedural control knowledge. This simplifies both the initial specification of a domain and the incremental learning of the control knowledge [Minton, 1988, Veloso and Carbonell, 1990] Previous work in the linear planner of PRODIGY used explanation based learning techniques [Minton, 1988] to extract from a problem solving trace the explanation chain responsible for a success or failure and compile search control rules. We are now extending this work to NOLIMIT, as well as developing a derivational analogy approach to acquire control knowledge [Carbonell, 1986, Veloso and ....

Steven Minton. Learning Effective Search Control Knowledge: An Explanation-BasedApproach. Kluwer Academic Publishers, Boston, MA, 1988.

....which supports our research goals underlying hamlet s learning algorithm. The overall running times also decreased using the rules, but not significantly. We did not find empirically with our learned rules that the time spent solving the problem degraded so much to consider it a utility problem [11]. However, we are currently developing efficient methods for organizing and matching the learned control rules. We consider this organization essential and part of the overall learning process [4] 5 Related work Most speedup learning systems have been applied to problem solvers with the ....

.... [4] 5 Related work Most speedup learning systems have been applied to problem solvers with the linearity assumption, such as the ones applied to Prolog or logic programming problem solvers [15, 21] special purpose problem solvers [12, 9, 18] or other general purpose linear problem solvers [5, 10, 11, 14]. These problem solvers are known to be incomplete and and incapable of finding optimal solutions. If we remove the linearity assumption, we are dealing with nonlinear problem solvers. This kind of problem solvers are needed to address real world complex problems. In general, there have not been ....

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Steven Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer Academic Publishers, Boston, MA, 1988.

....for solving efficiently any problem of the domain. Two approaches adopted have been to learn an explanation and prove that it is correct, or not prove that it is correct and refine it upon experiencing on other problems. The first approach, as in [ DeJong and Mooney, 1986, Mitchell et al. 1986, Minton, 1988 ] usually involves a substantial effort to prove the correctness of the learned knowledge. In addition it requires a complete domain theory to obtain the explanations , although there have some work on learning with incomplete, or intractable theories, such as [ Tadepalli, 1989 ] Moreover, ....

....can select, reject, prefer, or decide on the choice of alternatives [ Veloso, 1989 ] This knowledge guides the search process and helps to reduce the exponential explosion in the size of the search space. Previous work in the linear planner of prodigy uses explanation based learning techniques [ Minton, 1988 ] to extract from a problem solving trace the explanation chain responsible for a success or failure and compile search control rules therefrom. Similar efforts within the linear planner of prodigy were done to learn control rules from partially evaluating the domain theory [ Etzioni, 1990, ....

Steven Minton. Learning Effective Search Control Knowledge: An ExplanationBased Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988. Available as technical report CMU-CS-88-133.

....should be modified. The second computes the appropriate modification for each identified heuristic. The final part installs the modification into the problem solver s set of explicit search control rules. A restricted version of this algorithm has been implemented as an extension to the PRODIGY[13] problem solver. This extension is called Bacall. The main restriction is that Bacall can only modify the linearity[19] and the strong linearity [6] rejection heuristics. Linearity directs the problem solver to work on goals in a strict depth first fashion, i.e. pick a toplevel goal and an ....

....between the quality of the rejection rules learned and the cost of learning them. HAMLET is an example of a system that attempts such a compromise. While these systems learned more than just rejection search control rules, I will only discuss that one aspect. 2.2. 1 PRODIGY EBL PRODIGY EBL[13] learned rejection search control rules from failed search subtrees. The learner used a theory of the problem solver to identify the leaf failure reasons which were then regressed up the subtree. While the theory was relatively complete, it did not capture all of the relevant aspects of every ....

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S. Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer Academic Publishers, Boston, MA, 1988.

....guiding heuristics. One approach to learning control knowledge from a problem solving trace consists of generating explanations for the individual decisions made during the search process. These explanations become control strategies that are used in future situations to prune the search space [16]. There is also work done on doing the generation of control rules without problem solving episodes, by statically looking at the domain description [8] However, these strong deductive approaches invest a substantial explanation effort to produce correct control strategies from a single problem ....

....learned control knowledge to guide the search and convert it into an intelligent commitment search strategy [22] Control knowledge guides the search process and helps to prune the exponential search space. Previous work in the linear planner of prodigy uses explanation based learning techniques [16] to extract from a problem solving trace the explanation chain responsible for a success or failure and compile search control rules therefrom. Similar efforts within the linear planner of prodigy were done to learn control rules from partially evaluating the domain theory [8, 19] The paper ....

[Article contains additional citation context not shown here]

Steven Minton. Learning Effective Search Control Knowledge: An ExplanationBased Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988. Available as technical report CMU-CS-88-133.

....approaches. We hope to use ideas from Soar to address structure learning in Icarus. Prodigy [23] is an architecture for integrating planning and learning. Like Soar, it has been applied to execution systems, but the main emphasis has been on problem solving. An early version of Prodigy [12] applied explanation based learning to develop new rules that govern which states to expand or operators to select. More recent work adds learning methods with mutually interpretable knowledge structures, such as analogical reasoning and learning by experimentation. These methods support the goals ....

Minton, S. (1988). Learning effective search control knowledge: An explanation-based approach. Boston, MA: Kluwer Academic Publishers,.

....deposited and accessible by researchers in machine learning. we address the latter in the context of PRODIGY [Carbonell eta] 1990, Minton eta] 1989b, Ve]oso, 1989] a general purpose complete plan ner that incorporates various learning techniques: explanation based learning (EBL) [Minton, 1988], acquisition of control knowledge through static analysis [Etzioni, 1990] learning by analogy [Veloso, 1991] learning by experimentation [Carbonell and Gil, 1990] learning abstraction hierarchies for effective planning [Knoblock, 1991] and semiautomated knowledge acquisition interfaces ....

....learning time and run time overhead of acquiring and using the new knowledge with increasing complexity. At worst, the overhead should remain a constant fraction of overall problem solving, and at best it should be a diminishing fraction with increased domain size; otherwise the utility problem [Minton, 1988] will prove a serious hindrance. The synergistic combination of multiple learning techniques producing far more performance improvements than individual learning techniques, without paying a correspondingly large overhead cost [Knoblock et al. 1991] Measurements of performance with differ ent ....

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Minton, S. (1988). Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Computer Science Department, Carnegie Mellon University. Available as technical report CMU-CS-88-133.

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Steven Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988. 194

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Steven Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988.

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Steven Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988.

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Minton, S. (1988). Learning effective search control knowledge: An explanation-based approach. Boston, MA: Kluwer Academic Publishers.

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Minton, S. 1988. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Boston, MA: Kluwer Academic Publishers.

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Minton, S. 1988b. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Ph.D. Dissertation, Carnegie-Mellon University, Pittsburgh, PA.

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Minton, S. 1988a. Learning Effective Search Control Knowledge: An Explanation-Based Approach. Boston, MA: Kluwer Academic Publishers.

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Minton, S.: 1988, Learning Effective Search Control Knowledge: An Explanation-Based Approach, Boston, MA, Kluwer Academic, Dordrecht.

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Minton, S., (1988). Learning Effective Search Control Knowledge: An Explanation-based Approach, Doctoral dissertation, Department of Computer Science, Carnegie Mellon Univ.

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Minton, S. Learning Effective Search Control Knowledge: An explanation-based approach. PhD thesis, Computer Science Department, Carnegie Mellon University, 1988.

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S. Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Carnegie-Mellon University, Pittsburgh, PA, 1988.

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S. Minton. Learning Effective Search Control Knowledge: An Explanation-Based Approach. PhD thesis, Carnegie-Mellon University, Pittsburgh, PA, 1988.

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S. Minton. Learning Effective Search Control Knowledge: An Explanation-based Approach. Kluwer Academic Publishers, Boston, MA, 1988.

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