Download Constraint Solving and Planning with Picat by Neng-Fa Zhou, Håkan Kjellerstrand, Jonathan Fruhman PDF

By Neng-Fa Zhou, Håkan Kjellerstrand, Jonathan Fruhman

This booklet introduces a brand new logic-based multi-paradigm programming language that integrates good judgment programming, sensible programming, dynamic programming with tabling, and scripting, to be used in fixing combinatorial seek difficulties, together with CP, SAT, and MIP (mixed integer programming) established solver modules, and a module for making plans that's carried out utilizing tabling.

The publication turns out to be useful for undergraduate and graduate scholars, researchers, and practitioners.

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Example text

Creates an infinite list of positive integers. In Picat, the freeze(X,Call) predicate, which delays Call until X is instantiated, can be used to simulate lazy evaluation, and infinite data can be created through the use of backtracking. => X =1. gen(X) => gen(X1), X = X1+1. Picat is essentially a relational language. Logic variables and automatic backtracking are two features that make Picat more suitable for search problems than Haskell. The built-in predicate append is probably one of the most powerful and convenient nondeterministic predicates in Picat.

A CSP forms a search space. Solvers have techniques for reducing the search space, and use strategies to search the space for solutions. For example, CP and SAT solvers use constraint propagation to prune unfruitful paths in the search space. com/. -F. 1007/978-3-319-25883-6_2 33 34 2 Basic Constraint Modeling A decision variable is also called a domain variable. In Picat, the domain constraint X : : D can be used to restrict the domains of variables. A domain variable is a special kind of a logic variable.

3 . 3 . For this instance, the third cell in the first row has the value 2, which indicates that it has two adjacent mines. ” are unknowns, meaning that the cell may or may not have a mine. One can also observe that if there is a number in a cell, then the cell cannot contain a mine. 40 2 Basic Constraint Modeling import sat. length, % decision variables: where are the mines? NCols) % only check those cells that have hints if ground(Matrix[I,J]) then % The number of neighboring mines must equal Matrix[I,J].

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