An empirical evaluation of knowledge compilation by theory approximation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Control knowledge in planning: benefits and tradeoffs
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Solution of the Robbins Problem
Journal of Automated Reasoning
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A survey on knowledge compilation
AI Communications
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Stochastic search and phase transitions: AI meets physics
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Challenge problems for artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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In order to deal with the inherent combinatorial nature of many tasks in artificial intelligence, domain‐specific knowledge has been used to control search and reasoning or to eliminate the need for general inference altogether. However, the process of acquiring domain knowledge is an important bottleneck in the use of such “knowledge‐intensive” methods. Compute‐intensive methods, on the other hand, use extensive search and reasoning strategies to limit the need for detailed domain‐specific knowledge. The idea is to derive much of the needed information from a relatively compact formalization of the domain under consideration. Up until recently, such general reasoning strategies were much too expensive for use in applications of interesting size but recent advances in reasoning and search methods have shown that compute‐intensive methods provide a promising alternative to knowledge‐intensive methods.