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Constraint Programming aims at solving hard combinatorial problems, with a computation time increasing in practice exponentially. The methods are today efficient enough to solve large industrial problems, in a generic framework. However, solvers are dedicated to a single variable type: integer or real. Solving mixed problems relies on ad hoc transformations. In another field, Abstract Interpretation offers tools to prove program properties, by studying an abstraction of their concrete semantics, that is, the set of possible values of the variables during an execution. Various representations for these abstractions have been proposed. They are called abstract domains. Abstract domains can mix any type of variables, and even represent relations between the variables.
In this work, we define abstract domains for Constraint Programming, so as to build a generic solving method, dealing with both integer and real variables. We also study the octagons abstract domain, already defined in Abstract Interpretation. Guiding the search by the octagonal relations, we obtain good results on a continuous benchmark. We also define our solving method using Abstract Interpretation techniques, in order to include existing abstract domains. Our solver, AbSolute, is able to solve mixed problems and use relational domains.
- Exploits the over-approximation methods to integrate AI tools in the methods of CP
- Exploits the relationships captured to solve continuous problems more effectively
- Learn from the developers of a solver capable of handling practically all abstract domains
Researchers and graduate students in Constraint Programming, Researchers in Artificial Intelligence, Operations Research, and Mathematical Programming
<li>Introduction<ul><li>I.1 Context</li><li>I.2 Problematic</li><li>I.3 Outline of the book</li><li>I.4 Contributions</li></ul></li>
<li>1. State of the Art<ul><li>Abstract</li><li>1.1 Abstract interpretation</li><li>1.2 Constraint programming</li><li>1.3 Synthesis</li></ul></li>
<li>2. Abstract Interpretation for the Constraints<ul><li>Abstract</li><li>2.1 Introduction</li><li>2.2 Unified components</li><li>2.3 Unified solving</li><li>2.4 Conclusion</li></ul></li>
<li>3. Octagons<ul><li>Abstract</li><li>3.1 Definitions</li><li>3.2 Representations</li><li>3.3 Abstract domain components</li><li>3.4 Abstract domains</li><li>3.5 Conclusion</li></ul></li>
<li>4. Octagonal Solving<ul><li>Abstract</li><li>4.1 Octagonal CSP</li><li>4.2 Octagonal consistency and propagation</li><li>4.3 Octagonal solver</li><li>4.4 Experimental results</li><li>4.5 Conclusion</li></ul></li>
<li>5. An Abstract Solver: AbSolute<ul><li>Abstract</li><li>5.1 Abstract solving method</li><li>5.2 The AbSolute solver</li><li>5.3 Conclusion</li></ul></li>
<li>Conclusion and Perspectives<ul><li>C.1 Conclusion</li><li>C.2 Perspectives</li></ul></li>
- No. of pages:
- © ISTE Press - Elsevier 2015
- 6th May 2015
- ISTE Press - Elsevier
- Hardcover ISBN:
- eBook ISBN:
Marie Pelleau is a Doctorate in Computer Science at the University of Nantes, France. Her research interests include Constraint Programming and local research Constraint Programming (CP) to model and solve combinatory problems.
Université de Nantes, France
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