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Artificial Intelligence

Foundations, Theory, and Algorithms

By: Justyna Petke

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Hardcover | 19 August 2015

At a Glance

Hardcover
128 Pages

Dimensions(cm)
24.13 x 16.51 x 1.91

Hardcover

$84.99

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This book investigates the connections between constraint satisfaction problems (CSP) and Boolean satisfiability problems (SAT) and explains when we should choose a SAT-solver over a constraint solver, and vice versa. The author shows that with some encodings SAT-solvers simulate the effects of enforcing a form of local consistency in expected polynomial-time, which in turn explains why SAT-solvers are able to solve CSP instances of bounded-width structure efficiently, in contrast to conventional constraint solvers. The author first presents background notes on CSP and SAT, solver performance and SAT encodings, including a theoretical argument for the choice of the order encoding over the standard ones for several important classes of CSP instances. She provides a complete list of the constraint languages that are encoded to tractable language classes for SAT using the order encoding, and offers both theoretical and empirical comparison of the various SAT encodings of the famous pigeonhole problem. The book will be useful for researchers and graduate students in artificial intelligence and theoretical computer science.

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