Controlling backward inference
Artificial Intelligence
Acquiring search-control knowledge via static analysis
Artificial Intelligence
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Automatically generating abstractions for planning
Artificial Intelligence
On the utility of bottleneck reasoning for scheduling
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Inferring state constraints for domain-independent planning
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
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Extending the Smodels system with cardinality and weight constraints
Logic-based artificial intelligence
Declarative problem-solving using the DLV system
Logic-based artificial intelligence
Getting to the airport: the oldest planning problem in AI
Logic-based artificial intelligence
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
TALplanner: An Empirical Investigation of a Temporal Logic-based Forward Chaining Planner
TIME '99 Proceedings of the Sixth International Workshop on Temporal Representation and Reasoning
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A reactive planner for a model-based executive
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
When gravity fails: local search topology
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Linear time near-optimal planning in the blocks world
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Exploiting symmetry in lifted CSPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Getting to the airport: the oldest planning problem in AI
Logic-based artificial intelligence
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Propositional satisfiability checking is a powerful approach to domain-independent planning. In nearly all practical applications, however, there exists an abundance of domain-specific knowledge that can be used to improve the performance of a planning system. This knowledge is traditionally encoded as procedures or rules that are tied to the details of the planning engine. We present a way to encode domain knowledge in a purely declarative, algorithm independent manner. We demonstrate that the same heuristic knowledge can be used by completely different search engines, one systematic, the other using greedy local search. This approach enhances the power of planning as satisfiability : solution times for some problems are reduced from days to seconds.