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» Solving Over-Constrained Problems with SAT
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AIPS
2008
13 years 10 months ago
The Complexity of Optimal Planning and a More Efficient Method for Finding Solutions
We present a faster method of solving optimal planning problems and show that our solution performs up to an order of magnitude faster than Satplan on a variety of problems from t...
Katrina Ray, Matthew L. Ginsberg
ICCAD
2008
IEEE
140views Hardware» more  ICCAD 2008»
14 years 4 months ago
To SAT or not to SAT: Ashenhurst decomposition in a large scale
Functional decomposition is a fundamental operation in logic synthesis. Prior BDD-based approaches to functional decomposition suļ¬€er from the memory explosion problem and do not...
Hsuan-Po Lin, Jie-Hong Roland Jiang, Ruei-Rung Lee
JAIR
2007
132views more  JAIR 2007»
13 years 7 months ago
New Inference Rules for Max-SAT
Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simpliļ¬...
Chu Min Li, Felip Manyà, Jordi Planes
CP
2004
Springer
14 years 1 months ago
Understanding Random SAT: Beyond the Clauses-to-Variables Ratio
It is well known that the ratio of the number of clauses to the number of variables in a random k-SAT instance is highly correlated with the instanceā€™s empirical hardness. We con...
Eugene Nudelman, Kevin Leyton-Brown, Holger H. Hoo...
AI
2005
Springer
13 years 7 months ago
: Compiling problem specifications into SAT
We present a compiler that translates a problem specification into a propositional satisfiability test (SAT). Problems are specified in a logic-based language, called NP-SPEC, whi...
Marco Cadoli, Andrea Schaerf