Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Counting CSP solutions using generalized XOR constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Bayes networks for estimating the number of solutions to a CSP
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Probabilistic arc consistency: a connection between constraint reasoning and probabilistic reasoning
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Towards rational deployment of multiple heuristics in A*
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Heuristic search when time matters
Journal of Artificial Intelligence Research
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Heuristics are crucial tools in decreasing search effort in varied fields of AI. In order to be effective, a heuristic must be efficient to compute, as well as provide useful information to the search algorithm. However, some well-known heuristics which do well in reducing backtracking are so heavy that the gain of deploying them in a search algorithm might be outweighed by their overhead. We propose a rational metareasoning approach to decide when to deploy heuristics, using CSP backtracking search as a case study. In particular, a value of information approach is taken to adaptive deployment of solution-count estimation heuristics for value ordering. Empirical results show that indeed the proposed mechanism successfully balances the tradeoff between decreasing backtracking and heuristic computational overhead, resulting in a significant overall search time reduction.