Fast planning through planning graph analysis
Artificial Intelligence
Bounded Model Checking Using Satisfiability Solving
Formal Methods in System Design
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The LPSAT Engine & Its Application to Resource Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Deciding Quantifier-Free Presburger Formulas Using Parameterized Solution Bounds
LICS '04 Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science
Processes and continuous change in a SAT-based planner
Artificial Intelligence
MathSAT: Tight Integration of SAT and Mathematical Decision Procedures
Journal of Automated Reasoning
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Adapting an AI planning heuristic for directed model checking
SPIN'06 Proceedings of the 13th international conference on Model Checking Software
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Artificial Intelligence
Partial weighted MaxSAT for optimal planning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
SAS+ planning as satisfiability
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
Translation to Boolean satisfiability is an important approach for solving state-space reachability problems that arise in planning and verification. Many important problems, however, involve numeric variables; for example, C programs or planning with resources. Focussing on planning, we propose a method for translating such problems into propositional SAT, based on an approximation of reachable variable domains. We compare to a more direct translation into "SAT modulo theory" (SMT), that is, SAT extended with numeric variables and arithmetic constraints. Though translation to SAT generates much larger formulas, we show that it typically outperforms translation to SMT almost up to the point where the formulas don't fit into memory any longer. We also show that, even though our planner is optimal, it tends to outperform state-of-the-art sub-optimal heuristic planners in domains with tightly constrained resources. Finally we present encouraging initial results on applying the approach to model checking.