Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
On the construction of heuristic functions
On the construction of heuristic functions
Principles of artificial intelligence
Principles of artificial intelligence
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Artificial Intelligence
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Artificial Intelligence
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Machine Learning
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Machine Learning
Strengthening heuristics for lower cost optimal and near optimal solutions in A* search
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
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Computers & thought
Genetics-Based Learning of New Heuristics: Rational Scheduling of Experiments and Generalization
IEEE Transactions on Knowledge and Data Engineering
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning while solving problems in best first search
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Compressing Pattern Databases with Learning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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In this paper, we provide an overall framework for learning in search-based systems that are used to find optimum solutions to problems. This framework assumes that prior knowledge is available in the form of one or more heuristic functions (or features) of the problem domain. An appropriate clustering strategy is used to partition the state space into a number of classes based on the available features. The number of classes formed will depend on the resource constraints of the system. In the training phase, example problems are run using a standard admissible search algorithm. In this phase, heuristic information corresponding to each class is learned. This new information can be used in the problem-solving phase by appropriate search algorithms so that subsequent problem instances can be solved more efficiently. In this framework, we also show that heuristic information of forms other than the conventional single-valued underestimate value can be used, since we maintain the heuristic of each class explicitly. We show some novel search algorithms that can work with some such forms. Experimental results have been provided for some domains.