Extending the Gecode Framework with Interval Constraint Programming
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Constraint Satisfaction with Preferences Ph.D
MASARYK UNIVERSITY BRNO FACULTY OF INFORMATICS } Û¡¢£¤¥¦§¨ª«¬Æ !"#°±²³´µ·¸¹º»¼½¾¿45<ÝA| Constraint Satisfaction with Preferences Ph.D. Thesis Brno, January 2001 Hana Rudová ii Acknowledgements I would like to thank my supervisor, Ludˇek Matyska, for his continuous support, guidance, and encouragement. I am very grateful for his help and advice, which have allowed me to develop both as a person and as the avid student I am today. I want to thank to my husband and my family for their support, patience, and love during my PhD study and especially during writing of this thesis. This research was supported by the Universities Development Fund of the Czech Repub- lic under contracts # 0748/1998 and # 0407/1999. Declaration I declare that this thesis was composed by myself, and all presented results are my own, unless otherwise stated. Hana Rudová iii iv Contents 1 Introduction 1 1.1 Thesis Outline ..................................... 1 2 Constraint Satisfaction 3 2.1 Constraint Satisfaction Problem ........................... 3 2.2 Optimization Problem ................................ 4 2.3 Solution Methods ................................... 5 2.4 Constraint Programming ............................... 6 2.4.1 Global Constraints .............................. 7 3 Frameworks 11 3.1 Weighted Constraint Satisfaction .......................... 11 3.2 Probabilistic Constraint Satisfaction ........................ 12 3.2.1 Problem Definition .............................. 13 3.2.2 Problems’ Lattice ............................... 13 3.2.3 Solution -
Backtracking Search (Csps) ■Chapter 5 5.3 Is About Local Search Which Is a Very Useful Idea but We Won’T Cover It in Class
CSC384: Intro to Artificial Intelligence Backtracking Search (CSPs) ■Chapter 5 5.3 is about local search which is a very useful idea but we won’t cover it in class. 1 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ● The search algorithms we discussed so far had no knowledge of the states representation (black box). ■ For each problem we had to design a new state representation (and embed in it the sub-routines we pass to the search algorithms). ● Instead we can have a general state representation that works well for many different problems. ● We can build then specialized search algorithms that operate efficiently on this general state representation. ● We call the class of problems that can be represented with this specialized representation CSPs---Constraint Satisfaction Problems. ● Techniques for solving CSPs find more practical applications in industry than most other areas of AI. 2 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ●The idea: represent states as a vector of feature values. We have ■ k-features (or variables) ■ Each feature takes a value. Domain of possible values for the variables: height = {short, average, tall}, weight = {light, average, heavy}. ●In CSPs, the problem is to search for a set of values for the features (variables) so that the values satisfy some conditions (constraints). ■ i.e., a goal state specified as conditions on the vector of feature values. 3 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ●Sudoku: ■ 81 variables, each representing the value of a cell. ■ Values: a fixed value for those cells that are already filled in, the values {1-9} for those cells that are empty. -
Prolog Lecture 6
Prolog lecture 6 ● Solving Sudoku puzzles ● Constraint Logic Programming ● Natural Language Processing Playing Sudoku 2 Make the problem easier 3 We can model this problem in Prolog using list permutations Each row must be a permutation of [1,2,3,4] Each column must be a permutation of [1,2,3,4] Each 2x2 box must be a permutation of [1,2,3,4] 4 Represent the board as a list of lists [[A,B,C,D], [E,F,G,H], [I,J,K,L], [M,N,O,P]] 5 The sudoku predicate is built from simultaneous perm constraints sudoku( [[X11,X12,X13,X14],[X21,X22,X23,X24], [X31,X32,X33,X34],[X41,X42,X43,X44]]) :- %rows perm([X11,X12,X13,X14],[1,2,3,4]), perm([X21,X22,X23,X24],[1,2,3,4]), perm([X31,X32,X33,X34],[1,2,3,4]), perm([X41,X42,X43,X44],[1,2,3,4]), %cols perm([X11,X21,X31,X41],[1,2,3,4]), perm([X12,X22,X32,X42],[1,2,3,4]), perm([X13,X23,X33,X43],[1,2,3,4]), perm([X14,X24,X34,X44],[1,2,3,4]), %boxes perm([X11,X12,X21,X22],[1,2,3,4]), perm([X13,X14,X23,X24],[1,2,3,4]), perm([X31,X32,X41,X42],[1,2,3,4]), perm([X33,X34,X43,X44],[1,2,3,4]). 6 Scale up in the obvious way to 3x3 7 Brute-force is impractically slow There are very many valid grids: 6670903752021072936960 ≈ 6.671 × 1021 Our current approach does not encode the interrelationships between the constraints For more information on Sudoku enumeration: http://www.afjarvis.staff.shef.ac.uk/sudoku/ 8 Prolog programs can be viewed as constraint satisfaction problems Prolog is limited to the single equality constraint: – two terms must unify We can generalise this to include other types of constraint Doing so leads -
The Logic of Satisfaction Constraint
Artificial Intelligence 58 (1992) 3-20 3 Elsevier ARTINT 948 The logic of constraint satisfaction Alan K. Mackworth* Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6T 1 W5 Abstract Mackworth, A.K., The logic of constraint satisfaction, Artificial Intelligence 58 (1992) 3-20. The constraint satisfaction problem (CSP) formalization has been a productive tool within Artificial Intelligence and related areas. The finite CSP (FCSP) framework is presented here as a restricted logical calculus within a space of logical representation and reasoning systems. FCSP is formulated in a variety of logical settings: theorem proving in first order predicate calculus, propositional theorem proving (and hence SAT), the Prolog and Datalog approaches, constraint network algorithms, a logical interpreter for networks of constraints, the constraint logic programming (CLP) paradigm and propositional model finding (and hence SAT, again). Several standard, and some not-so-standard, logical methods can therefore be used to solve these problems. By doing this we obtain a specification of the semantics of the common approaches. This synthetic treatment also allows algorithms and results from these disparate areas to be imported, and specialized, to FCSP; the special properties of FCSP are exploited to achieve, for example, completeness and to improve efficiency. It also allows export to the related areas. By casting CSP both as a generalization of FCSP and as a specialization of CLP it is observed that some, but not all, FCSP techniques lift to CSP and thereby to CLP. Various new connections are uncovered, in particular between the proof-finding approaches and the alternative model-finding ap- proaches that have arisen in depiction and diagnosis applications. -
Estimating Parallel Runtimes for Randomized Algorithms in Constraint Solving Charlotte Truchet, Alejandro Arbelaez, Florian Richoux, Philippe Codognet
Estimating parallel runtimes for randomized algorithms in constraint solving Charlotte Truchet, Alejandro Arbelaez, Florian Richoux, Philippe Codognet To cite this version: Charlotte Truchet, Alejandro Arbelaez, Florian Richoux, Philippe Codognet. Estimating par- allel runtimes for randomized algorithms in constraint solving. Journal of Heuristics, Springer Verlag, 2015, pp.1-36. <10.1007/s10732-015-9292-3>. <hal-01248168> HAL Id: hal-01248168 https://hal.archives-ouvertes.fr/hal-01248168 Submitted on 24 Dec 2015 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Journal of Heuristics manuscript No. (will be inserted by the editor) Estimating Parallel Runtimes for Randomized Algorithms in Constraint Solving Charlotte Truchet · Alejandro Arbelaez · Florian Richoux · Philippe Codognet the date of receipt and acceptance should be inserted later Abstract This paper presents a detailed analysis of the scalability and par- allelization of Local Search algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a framework to estimate the parallel perfor- mance of a given algorithm by analyzing the runtime behavior of its sequential version. Indeed, by approximating the runtime distribution of the sequential process with statistical methods, the runtime behavior of the parallel process can be predicted by a model based on order statistics. -
Constraint Satisfaction Problems (Backtracking Search)
CSC384: Introduction to Artificial Intelligence Constraint Satisfaction Problems (Backtracking Search) • Chapter 6 – 6.1: Formalism – 6.2: Constraint Propagation – 6.3: Backtracking Search for CSP – 6.4 is about local search which is a very useful idea but we won’t cover it in class. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 1 Acknowledgements • Much of the material in the lecture slides comes from Fahiem Bacchus, Sheila McIlraith, and Craig Boutilier. • Some slides come from a tutorial by Andrew Moore via Sonya Allin. • Some slides are modified or unmodified slides provided by Russell and Norvig. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 2 Constraint Satisfaction Problems (CSP) • The search algorithms we discussed so far had no knowledge of the states representation (black box). – For each problem we had to design a new state representation (and embed in it the sub-routines we pass to the search algorithms). • Instead we can have a general state representation that works well for many different problems. • We can then build specialized search algorithms that operate efficiently on this general state representation. • We call the class of problems that can be represented with this specialized representation: CSPs – Constraint Satisfaction Problems. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 3 Constraint Satisfaction Problems (CSP) •The idea: represent states as a vector of feature values. – k-features (or variables) – Each feature takes a value. Each variable has a domain of possible values: • height = {short, average, tall}, • weight = {light, average, heavy} •In CSPs, the problem is to search for a set of values for the features (variables) so that the values satisfy some conditions (constraints). -
Constraint Satisfaction
Constraint Satisfaction Rina Dechter Department of Computer and Information Science University of California, Irvine Irvine, California, USA 92717 dechter@@ics.uci.edu A constraint satisfaction problem csp de ned over a constraint network consists of a nite set of variables, each asso ciated with a domain of values, and a set of constraints. A solution is an assignmentofavalue to each variable from its domain such that all the constraints are satis ed. Typical constraint satisfaction problems are to determine whether a solution exists, to nd one or all solutions and to nd an optimal solution relative to a given cost function. An example of a constraint satisfaction problem is the well known k -colorability. The problem is to color, if p ossible, a given graph with k colors only, such that anytwo adjacent no des have di erent colors. A constraint satisfaction formulation of this problem asso ciates the no des of the graph with variables, the p ossible colors are their domains and the inequality constraints b etween adjacent no des are the constraints of the problem. Each constraint of a csp may b e expressed as a relation, de ned on some subset of variables, denoting their legal combinations of values. As well, constraints 1 can b e describ ed by mathematical expressions or by computable pro cedures. Another known constraint satisfaction problem is SATis ability; the task of nding the truth assignment to prop ositional variables such that a given set of clauses are satis ed. For example, given the two clauses A _ B _ :C ; :A _ D , the assignmentof false to A, true to B , false to C and false to D , is a satisfying truth value assignment. -
Aggregation and Garbage Collection for Online Optimization⋆
Aggregation and Garbage Collection for Online Optimization? Alexander Ek1;2[0000−0002−8744−4805], Maria Garcia de la Banda1[0000−0002−6666−514X], Andreas Schutt2[0000−0001−5452−4086], Peter J. Stuckey1;2[0000−0003−2186−0459], and Guido Tack1;2[0000−0003−2186−0459] 1 Monash University, Australia fAlexander.Ek,Maria.GarciadelaBanda,Peter.Stuckey,[email protected] 2 Data61, CSIRO, Australia [email protected] Abstract Online optimization approaches are popular for solving op- timization problems where not all data is considered at once, because it is computationally prohibitive, or because new data arrives in an on- going fashion. Online approaches solve the problem iteratively, with the amount of data growing in each iteration. Over time, many problem variables progressively become realized, i.e., their values were fixed in the past iterations and they can no longer affect the solution. If the solv- ing approach does not remove these realized variables and associated data and simplify the corresponding constraints, solving performance will slow down significantly over time. Unfortunately, simply removing realized variables can be incorrect, as they might affect unrealized de- cisions. This is why this complex task is currently performed manually in a problem-specific and time-consuming way. We propose a problem- independent framework to identify realized data and decisions, and re- move them by summarizing their effect on future iterations in a compact way. The result is a substantially improved model performance. 1 Introduction Online optimization tackles the solving of problems that evolve over time. In online optimization problems, in some areas also called dynamic or reactive, the set of input data is only partially known a priori and new data, such as new customers and/or updated travel times in a dynamic vehicle routing problem, continuously or periodically arrive while the current solution is executed. -
Propositional Satisfiability and Constraint Programming
Propositional Satisfiability and Constraint Programming: A Comparative Survey LUCAS BORDEAUX, YOUSSEF HAMADI and LINTAO ZHANG Microsoft Research Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively in- dependent threads of research cross-fertilizing occasionally. These two approaches to problem solving have a lot in common as evidenced by similar ideas underlying the branch and prune algorithms that are most successful at solving both kinds of problems. They also exhibit differences in the way they are used to state and solve problems since SAT’s approach is, in general, a black-box approach, while CP aims at being tunable and programmable. This survey overviews the two areas in a comparative way, emphasizing the similarities and differences between the two and the points where we feel that one technology can benefit from ideas or experience acquired from the other. Categories and Subject Descriptors: F.4.1 [Mathematical Logic and Formal Languages]: Mathematical Logic—Logic and constraint programming; D.3.3 [Programming Languages]: Language Constructs and Features—Constraints; G.2.1 [Discrete Mathematicals]: Combinatorics—Combinatorial algorithms General Terms: Algorithms Additional Key Words and Phrases: Search, constraint satisfaction, SAT ACM Reference Format: Bordeaux, L., Hamadi, Y., and Zhang, L. 2006. Propositional satisfiability and constraint programming: A comparative survey. ACM Comput. Surv. 38, 4, Article 12 (Dec. 2006), 54 pages. DOI = 10.1145/1177352. 1177354 http://doi.acm.org/10.1145/1177352.1177354 1. INTRODUCTION Propositional satisfiability solving (SAT) and constraint programming (CP) are two automated reasoning technologies that have found considerable industrial applications during the last decades. Both approaches provide generic languages that can be used to express complex (typically NP-complete) problems in areas like hardware verification, configuration, or scheduling. -
Techniques for Efficient Constraint Propagation
Techniques for Efficient Constraint Propagation MIKAEL Z. LAGERKVIST Licentiate Thesis Stockholm, Sweden, 2008 TRITA-ICT/ECS AVH 08:10 ISSN 1653-6363 KTH ISRN KTH/ICT/ECS AVH-08/10–SE SE-100 44 Stockholm ISBN 978-91-7415-154-1 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan fram- lägges till offentlig granskning för avläggande av teknologie licentiatexamen fredagen den 21 November 2008 klockan 13.15 i N2, Electrum 3, Kista. © Mikael Z. Lagerkvist, 2008 Tryck: Universitetsservice US AB On two occasions I have been asked, ’Pray, Mr. Babbage, if you put into the machine wrong figures, will the right an- swers come out?’ ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. Passages from the Life of a Philosopher Charles Babbage v Abstract This thesis explores three new techniques for increasing the efficiency of constraint propagation: support for incremental propagation, im- proved representation of constraints, and abstractions to simplify prop- agation. Support for incremental propagation is added to a propagator- centered propagation system by adding a new intermediate layer of abstraction, advisors, that capture the essential aspects of a variable- centered system. Advisors are used to give propagators a detailed view of the dynamic changes between propagator runs. Advisors enable the implementation of optimal algorithms for important constraints such as extensional constraints and Boolean linear in-equations, which is not possible in a propagator-centered system lacking advisors. Using Multivalued Decision Diagrams (MDD) as the representa- tion for extensional constraints is shown to be useful for several rea- sons. -
FOUNDATIONS of CONSTRAINT SATISFACTION Edward Tsang
FOUNDATIONS OF CONSTRAINT SATISFACTION Edward Tsang Department of Computer Science University of Essex Colchester Essex, UK Copyright 1996 by Edward Tsang All rights reserved. No part of this book may be reproduced in any form by photostat, microfilm, or any other means, without written permission from the author. Copyright 1993-95 by Academic Press Limited This book was first published by Academic Press Limited in 1993 in UK: 24-28 Oval Road, London NW1 7DX USA: Sandiego, CA 92101 ISBN 0-12-701610-4 To Lorna Preface Many problems can be formulated as Constraint Satisfaction Problems (CSPs), although researchers who are untrained in this field sometimes fail to recognize them, and consequently, fail to make use of specialized techniques for solving them. In recent years, constraint satisfaction has come to be seen as the core problem in many applications, for example temporal reasoning, resource allocation, schedul- ing. Its role in logic programming has also been recognized. The importance of con- straint satisfaction is reflected by the abundance of publications made at recent conferences such as IJCAI-89, AAAI-90, ECAI-92 and AAAI-92. A special vol- ume of Artificial Intelligence was also dedicated to constraint reasoning in 1992 (Vol 58, Nos 1-3). The scattering and lack of organization of material in the field of constraint satisfac- tion, and the diversity of terminologies being used in different parts of the literature, make this important topic more difficult to study than is necessary. One of the objectives of this book is to consolidate the results of CSP research so far, and to enable newcomers to the field to study this problem more easily. -
Model-Based Optimization for Effective and Reliable Decision-Making
Model-Based Optimization for Effective and Reliable Decision-Making Robert Fourer [email protected] AMPL Optimization Inc. www.ampl.com — +1 773-336-2675 DecisionCAMP Bolzano, Italy — 18 September 2019 Model-Based Optimization DecisionCAMP — 18 September 2019 1 Model-Based Optimization for Effective and Reliable Decision-Making Optimization originated as an advanced between the human modeler’s formulation mathematical technique, but it has become an and the solver software’s needs. This talk accessible and widely used decision-making introduces model-based optimization by tool. A key factor in the spread of successful contrasting it to a method-based approach optimization applications has been the that relies on customized implementation of adoption of a model-based approach: A rules and algorithms. Model-based domain expert or operations analyst focuses implementations are illustrated using the on modeling the problem of interest, while AMPL modeling language and popular the computation of a solution is left to solvers. The presentation concludes by general-purpose, off-the-shelf solvers; surveying the variety of modeling languages powerful yet intuitive modeling software and solvers available for model-based manages the difficulties of translating optimization today. Dr. Fourer has over 40 years’ experience in institutes, and corporations worldwide; he is studying, creating, and applying large-scale also author of a popular book on AMPL. optimization software. In collaboration with Additionally, he has been a key contributor to the colleagues in Computing Science Research at NEOS Server project and other efforts to make Bell Laboratories, he initiated the design and optimization services available over the Internet, development of AMPL, which has become one and has supported development of open-source of the most widely used software systems for software for operations research through his modeling and analyzing optimization problems, service on the board of the COIN-OR with users in hundreds of universities, research Foundation.