6 edition of **Complexity Classifications of Boolean Constraint Satisfaction Problems (Monographs on Discrete Mathematics and Applications)** found in the catalog.

- 221 Want to read
- 21 Currently reading

Published
**January 1, 1987**
by Society for Industrial Mathematics
.

Written in English

- Applied mathematics,
- Combinatorics & graph theory,
- Mathematical logic,
- Mathematical theory of computation,
- Constraints (Artificial intelligence),
- Boolean algebra,
- Mathematics,
- Science/Mathematics,
- Logic,
- Computers / General,
- Algebra - General,
- Computational complexity,
- Constraints (Artificial intell

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 118 |

ID Numbers | |

Open Library | OL8271884M |

ISBN 10 | 0898714796 |

ISBN 10 | 9780898714791 |

The Complexity of Constraint Satisfaction Games and QCSP Ferdinand B˜orner Institut fur˜ Informatik University of Potsdam Potsdam, D, Germany [email protected] An. Read "Complexity Dichotomies for Counting Problems: Volume 1, Boolean Domain" by Jin-Yi Cai available from Rakuten Kobo. Complexity theory aims to understand and classify computational problems, especially decision problems, according to the.

A boolean constraint satisfaction problem consists of some finite set of constraints (i.e., functions from 0 / 1-vectors to {0,1}) and an instance of such a problem is a set of constraints applied to specified subsets of n boolean variables. The goal is to find an assignment to the variables which satisfy all constraint applications. Nowadays constraint satisfaction problems (CSPs) are ubiquitous in many different areas of computer science, from artificial intelligence and database systems to circuit design, network optimization, and theory of programming languages. Consequently, it is important to analyze and pinpoint the.

In the computational complexity theory of counting problems, a polynomial-time counting reduction is a type of reduction (a transformation from one problem to another) used to define the notion of completeness for the complexity class ♯P. These reductions may also be called polynomial many-one counting reductions or weakly parsimonious reductions; they are analogous to many-one reductions. The Complexity of Soft Constraint Satisfaction David A. Cohen, Department of Computer Science, Royal Holloway, University of London, UK @ Martin C. Cooper, IRIT.

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Complexity classifications of boolean constraint satisfaction problems is devoted to the study of the complexity of such problems." The book is a monograph, containing barely pages of text, and is necessarily densely packed with theorems, lemmas, and proofs.

is used to define P-hardness and P-completeness. Chapter 3 formally. Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction problems (CSP).

This book is devoted to the study of the complexity of such problems. The authors' goal is to develop a framework for classifying the. ISBN: OCLC Number: Description: xii, pages ; 26 cm. Contents: 1. Introduction Complexity classes Boolean constraint satisfaction problems Characterizations of constraint functions Implementation of functions and reductions Classification theorems for decision, counting and quantified problems Classification theorems.

Get this from a library. Complexity classifications of Boolean constraint satisfaction problems. [Nadia Creignou; Sanjeev Khanna; Madhu Sudan] -- Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction.

Request PDF | Complexity Classifications of Boolean Constraint Satisfaction Problems | Preface 1. Introduction 2.

Complexity Classes 3. Boolean Constraint Satisfaction Problems 4. We introduce Boolean constraint satisfaction problems in this chapter.

We define several classes of constraint satisfaction problems, corresponding to the different classes NP, PSPACE, NC, #P, and NPO.

The unifying concept behind all the classes, and all problems within a class, is the presentation of the input instances. The complexity of constraint satisfaction is the application of computational complexity theory on constraint has mainly been studied for discriminating between tractable and intractable classes of constraint satisfaction problems on finite domains.

Solving a constraint satisfaction problem on a finite domain is an NP-complete problem in general. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory.

Schaefer proved in that the Boolean constraint satisfaction problem for a given constraint language is either in P or is NP-complete, and identified all tractable cases. Schaefer’s dichotomy theorem actually shows that there are at most two constraint satisfaction problems, up to polynomial-time isomorphism (and these isomorphism types.

Abstract. A Boolean constraint satisfaction instance is a set of constraint applications where the allowed constraints are drawn from a fixed set C of Boolean functions. We consider the problem of determining whether two given constraint satisfaction instances are equivalent and prove a dichotomy theorem by showing that for all finite sets C of constraints, this problem is either polynomial.

Constraint Satisfaction Problems: Complexity and Algorithms Andrei A. Bulatov1, Simon Fraser University In this paper we brieﬂy survey the history of the Dichotomy Conjecture for the Constraint Satisfaction prob-lem, that was posed 25 years ago by Feder and Vardi.

We outline some of the approaches to this conjecture. The study of constraint satisfaction occupies a prominent place in artificial intelligence, because many problems that arise in different areas can be modelled as constraint-satisfaction problems. 5 CONSTRAINT SATISFACTION PROBLEMS In which we see how treating states as more than just little black boxes leads to the invention of a range of powerful new search methods and a deeper understanding of problem structure and complexity.

Chapters 3 and 4 explored the idea that problems can be solved by searching in a space of states. ALGORITHMIC COMPLEXITY OF SOME CONSTRAINT SATISFACTION PROBLEMS Daya Ram Gaur B.

Tech. Computer Science and Engineering Institute of Technology, Banaras Hindu Univ. Varansi, India. A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in the School of Computing Science @ Daya Ram Gaur SIMON.

The complexity of constraint satisfaction revisited 59 One of the key insights of arc consistency for FCSPs can be found in Fikes' paper in the very first issue of Artificial Intelligence [6]; in particular, if a value, c, for one problem variable is inconsistent with all values for.

“The book is concerned with valued constraint satisfaction problems with finite domains, i. e., the problem of minimising the sum of functions that each depend on some subset of a finite set of discrete variables with finite domains.

In all places the description is very. My thesis is concerned with Boolean constraint satisfaction problems (CSP). A constraint consists of a set of variables and a (Boolean) relation, which restricts the assignment of certain tuples of variables.

A CSP then is the question, whether there is an assignment for all variables to a given set of constraints, such that all constraints are. A dichotomy theorem in computational complexity shows that every problem in a certain family of problems is either polynomial-time solvable or NP-complete.

The rst such result is Schaefer’s Dichotomy Theorem [15], which considers boolean constraint satisfaction. Let F be a nite set of boolean. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems.

The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This book is the only source for an extended, concentrated focus on the theory and techniques common to various types of intersection graphs.

It is a concise treatment of the aspects of intersection graphs that interconnect many standard concepts and form the foundation of a surprising array of applications to biology, computing, psychology.

problems is either polynomial time solvable or NP-complete. The ﬂrst such result is Schaefer’sDichotomy Theorem [14], which considers boolean constraint sat-isfaction. Let F be a ﬂnite set of boolean constraints, each constraint is a boolean relation of some ﬂnite ar-ity.

In the F-SAT problem we are given a formula that.The complexity of Boolean surjective general-valued CSPs PETER FULLA,University of Oxford, UK HANNES UPPMAN,Linköping University, Sweden STANISLAV ŽIVNÝ,University of Oxford, UK Valued constraint satisfaction problems (VCSPs) are discrete optimisation problems with a (Q∪{∞})-valued objective function given as a sum of fixed-arity functions.On this version of the “Blue Book” This version of “The Complexity of Boolean Functions,” for some people simply the “Blue Book” due to the color of the cover of the orig-inal fromis not a print-out of the original sources.

It is rather a “facsimile” of the original monograph typeset in LATEX.