Pushing the envelope: planning, propositional logic, and stochastic search

Authors:
Henry Kautz;Bart Selman
Affiliations:
AT&T Laboratories, Murray Hill, NJ;AT&T Laboratories, Murray Hill, NJ
Venue:
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Year:
1996

Citing 15
Cited 264

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Automated verification of state sequence invariants in general game playing

Artificial Intelligence
Exploiting step semantics for efficient bounded model checking of asynchronous systems

Science of Computer Programming
Gearing up for effective ASP planning

Correct Reasoning
SAS+ planning as satisfiability

Journal of Artificial Intelligence Research
On the utility of landmarks in SAT based planning

Knowledge-Based Systems
Planning as satisfiability: Heuristics

Artificial Intelligence
On the complexity of planning for agent teams and its implications for single agent planning

Artificial Intelligence
Local backbones

SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Computing upper bounds on lengths of transition sequences

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A SAT-based approach to cost-sensitive temporally expressive planning

ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Automating the evaluation of planning systems

AI Communications

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Abstract

Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning systems. Although stochastic methods have been shown to be very effective on a wide range of scheduling problems, this is the first demonstration of its power on truly challenging classical planning instances. This work also provides a new perspective on representational issues in planning.