Stochastic Modeling of Branch-and-Bound Algorithms with Best-First Search
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Principles of artificial intelligence
Principles of artificial intelligence
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Efficiency and Completeness of the Set of Support Strategy in Theorem Proving
Journal of the ACM (JACM)
The Power of Dominance Relations in Branch-and-Bound Algorithms
Journal of the ACM (JACM)
Learning and reasoning by analogy
Communications of the ACM
Knowledge-Based Systems in Artificial Intelligence: 2 Case Studies
Knowledge-Based Systems in Artificial Intelligence: 2 Case Studies
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Fundamentals of Computer Alori
Fundamentals of Computer Alori
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Automated Reasoning: Introduction and Applications
Automated Reasoning: Introduction and Applications
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Efficient combinatorial search algorithms
Efficient combinatorial search algorithms
Intelligent mapping of communicating processes in distributed computing systems
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
Population-Based Learning: A Method for Learning from Examples Under Resource Constraints
IEEE Transactions on Knowledge and Data Engineering
Genetics-Based Learning of New Heuristics: Rational Scheduling of Experiments and Generalization
IEEE Transactions on Knowledge and Data Engineering
DD* lite: efficient incremental search with state dominance
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A generic method for identifying and exploiting dominance relations
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Dominance relations are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.