Constraint Propagation as Information Maximization

Details Constraint Propagation as Information Maximization Constraint Propagation as Information Maximization

2012-01-26

arxiv.org/abs/1201.5426v1

Computer Science

Artificial Intelligence

20 pages

Scientific paper

Dana Scott used the partial order among partial functions for his mathematical model of recursively defined functions. He interpreted the partial order as one of information content. In this paper we elaborate on Scott's suggestion of regarding computation as a process of information maximization by applying it to the solution of constraint satisfaction problems. Here the method of constraint propagation can be interpreted as decreasing uncertainty about the solution -- that is, as gain in information about the solution. As illustrative example we choose numerical constraint satisfaction problems to be solved by interval constraints. To facilitate this approach to constraint solving we formulate constraint satisfaction problems as formulas in predicate logic. This necessitates extending the usual semantics for predicate logic so that meaning is assigned not only to sentences but also to formulas with free variables.

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