ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Formalizing Commonsense: Papers by John McCarthy
Formalizing Commonsense: Papers by John McCarthy
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Application of theorem proving to problem solving
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
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
Bounded Model Checking for Timed Systems
FORTE '02 Proceedings of the 22nd IFIP WG 6.1 International Conference Houston on Formal Techniques for Networked and Distributed Systems
Dependent and Independent Variables in Propositional Satisfiability
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Model Checking Syllabi and Student Carreers
TACAS 2001 Proceedings of the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Integrating BDD-Based and SAT-Based Symbolic Model Checking
FroCoS '02 Proceedings of the 4th International Workshop on Frontiers of Combining Systems
CL '00 Proceedings of the First International Conference on Computational Logic
Applying the Davis-Putnam Procedure to Non-clausal Formulas
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Integer optimization models of AI planning problems
The Knowledge Engineering Review
Unrestricted vs restricted cut in a tableau method for Boolean circuits
Annals of Mathematics and Artificial Intelligence
Processes and continuous change in a SAT-based planner
Artificial Intelligence
Complexity results on DPLL and resolution
ACM Transactions on Computational Logic (TOCL)
Planning as satisfiability with preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A unifying framework for structural properties of CSPs: definitions, complexity, tractabilit
Journal of Artificial Intelligence Research
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
To encode or not to encode-1: linear planning
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Processes and continuous change in a SAT-based planner
Artificial Intelligence
Verifying Industrial Hybrid Systems with MathSAT
Electronic Notes in Theoretical Computer Science (ENTCS)
Limitations of restricted branching in clause learning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Propelling SAT and SAT-based BMC using careset
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
Building efficient decision procedures on top of SAT solvers
SFM'06 Proceedings of the 6th international conference on Formal Methods for the Design of Computer, Communication, and Software Systems
| Hi-index | 0.00 |
In this paper we focus on Planning as Satisfiability (SAT). We build from the simple consideration that the values of fiuents at a certain time point derive deterministically from the initial situation and the sequence of actions performed till that point. Thus, the choice of actions to perform is the only source of nondeterminism. This is a rather trivial consideration, but which has important positive consequences if implemented in current planners via SAT. In fact, it produces a dramatic size reduction of the space of the truth assignments searched in by the SAT decider used to solve the final SAT problem. To justify this claim, we repeat many of the experiments reported in (Ernst, Millstein, & Weld 1997), and show that the CPU time requested to solve a problem can go down up to 4 orders of magnitude.