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Khanh P.V. Doan, Kit Po Wong, "SHAPES: A Novel Approach for Learning Search Heuristics in UnderConstrained Optimization Problems," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 5, pp. 731746, SeptemberOctober, 1997.  
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@article{ 10.1109/69.634752, author = {Khanh P.V. Doan and Kit Po Wong}, title = {SHAPES: A Novel Approach for Learning Search Heuristics in UnderConstrained Optimization Problems}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {9}, number = {5}, issn = {10414347}, year = {1997}, pages = {731746}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.634752}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  SHAPES: A Novel Approach for Learning Search Heuristics in UnderConstrained Optimization Problems IS  5 SN  10414347 SP731 EP746 EPD  731746 A1  Khanh P.V. Doan, A1  Kit Po Wong, PY  1997 KW  ExplanationBased Learning KW  heuristics KW  intractability KW  search KW  weight assignment. VL  9 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—Although much research in machine learning has been carried out on acquiring knowledge for problemsolving in many problem domains, little effort has been focused on learning searchcontrol knowledge for solving optimization problems. This paper reports on the development of SHAPES, a system that learns heuristic search guidance for solving optimization problems in intractable, underconstrained domains based on the ExplanationBased Learning (EBL) framework. The system embodies two new and novel approaches to machine learning. First, it makes use of explanations of varying levels of approximation as a mean for verifying heuristicbased decisions, allowing heuristic estimates to be revised and corrected during problemsolving. The provision of such a revision mechanism is particularly important when working in intractable and underconstrained domains, where heuristics tend to be highly overgeneralized, and hence at times will give rise to incorrect results. Second, it employs a new linear and quadratic programmingbased weightassignment algorithm formulated to direct search toward optimal solutions under bestfirst search. The algorithm offers a direct method for assigning rule strengths and, in so doing, avoids the need to address the
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