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Constraint Solving and Planning with Picat

Part of the SpringerBriefs in Intelligent Systems Book Series

DOI: 10.1007/978-3-319-25883-6

ISBN: 978-3-319-25881-2

Publisher: Springer International Publishing - Springer Journals

Nov 8, 2015

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Chapter 1An Overview of Picat

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 1 –31

[Picat is a general-purpose programming language that includes features from multiple programming paradigmsProgramming paradigm. By combining imperative programming’s control flow features with more advanced features from logic programming, functional programming, and scripting, Picat provides...
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Chapter 2Basic Constraint Modeling

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 33 –51

[Given a set of variables, each of which has a domain of possible values, and a set of constraints that limit the acceptable set of assignments of values to variables, the goal of a CSP (Constraint Satisfaction Problem) is to find an assignment of values to the variables that satisfies all of the...
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Chapter 3Advanced Constraint Modeling

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 53 –82

[This chapter describes more advanced techniques of constraint modeling in Picat, as well as some tips about performance and debugging. The Langford number problem shows the use of the global constraint element and the concepts of reversibility and symmetry breaking. Reification, or reasoning...
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Chapter 4Dynamic Programming with Tabling

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 83 –99

[Tabling is a kind of memoizationMemoization technique that caches the results of certain calculations in memory and reuses them in subsequent calculations through a quick table lookup. Tabling makes declarative dynamic programming possible. Users only need to describe how to break a problem into...
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Chapter 5From Dynamic Programming to Planning

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 101 –113

[Planning can be treated as dynamic programming, for which tabling has been shown to be effective. Picat provides a module, named planner, which is based onPlanner tabling, but provides a level of abstraction that hides tabling from users. This chapter focuses on depth-unbounded search predicates...
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Chapter 6Planning with Resource-Bounded Search

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 115 –128

[In addition to depth-unbounded search predicatesDepth-unbounded search, Picat’s planner also provides resource-bounded search predicatesResource-bounded search. Resource-bounded search amounts to utilizing tabled states and their resource amounts to effectively decide when a state should be...
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Chapter 7Encodings for the Traveling Salesman Problem

Zhou, Neng-Fa; Kjellerstrand, Håkan; Fruhman, Jonathan

Pages: 129 –139

[So far, this book has presented several techniques for modeling constraint satisfaction, dynamic programming, and planning problems. This chapter presents encodings for the Traveling Salesman Problem, comparing models for several solvers, including CP, SAT, MIP, and tabled planning.]

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