Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
Fast planning through planning graph analysis
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
Investigating the effect of relevance and reachability constraints on SAT encodings of planning
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
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
To Encode or Not to Encode - Linear Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reachability, Relevance, Resolution and the Planning as Satisfiability Approach
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Ignoring Irrelevant Facts and Operators in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Understanding and Extending Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical 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
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
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Structured reachability analysis for Markov decision processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Long-distance mutual exclusion for propositional planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
Friends or foes? on planning as satisfiability and abstract CNF encodings
Journal of Artificial Intelligence Research
Planning with SAT, admissible heuristics and A*
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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In recent years, there is a growing awareness of the importance of reachability and relevance-based pruning techniques for planning, but little work specifically targets these techniques. In this paper, we compare the ability of two classes of algorithms to propagate and discover reachability and relevance constraints in classical planning problems. The first class of algorithms operates on SAT encoded planning problems obtained using the linear and GRAPHPLAN encoding schemes. It applies unit-propagation and more general resolution steps (involving larger clauses) to these plan encodings. The second class operates at the plan level and contains two families of pruning algorithms: Reachable-k and Relevant-k . Reachable-k provides a coherent description of a number of existing forward pruning techniques used in numerous algorithms, while Relevant-k captures different grades of backward pruning. Our results shed light on the ability of different plan-encoding schemes to propagate information forward and backward and on the relative merit of plan-level and SAT-level pruning methods.