Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Performance of linear-space search algorithms
Artificial Intelligence
Optimal composition of real-time systems
Artificial Intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Lecture notes on approximation algorithms: Volume I
Lecture notes on approximation algorithms: Volume I
An expected-cost analysis of backtracking and non-backtracking algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Searching for an optimal path in a tree with random costs
Artificial Intelligence
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Depth-first vs. best-first search: new results
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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In the real world, insufficient information, limited computation resources, and complex problem structures often force an autonomous agent to make a decision in time less than that required to solve the problem at hand completely. Flexible and approximate computation are two approaches to decision making under limited computation resources. Flexible computation helps an agent to flexibly allocate limited computation resources so that the overall system utility is maximized. Approximate computation enables an agent to find the best satisfactory solution within a deadline. In this paper, we present two state-space reduction methods for flexible and approximate computation: quantitative reduction to deal with inaccurate heuristic information, and structural reduction to handle complex problem structures. These two methods can be applied successively to continuously improve solution quality if more computation is available. Our results show that these reduction methods are effective and efficient, finding better solutions with less computation than some existing well-known methods.