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Author: Norman Sadeh Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 51
Book Description
Our investigation is conducted in the domain of job shop scheduling. It is shown that, in this domain, generic CSP heuristics are usually not sufficient to guide the search for a feasible solution. This is because these heuristics fail to properly account for the tightness of constraints and/or the connectivity of the constraint graph. Instead, a probabilistic model of the search space is used to define new heuristics, which better account for these problem characteristics. Experimental results indicate that these new heuristics yield important improvements in both search efficiency and search time."
Author: Norman Sadeh Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 51
Book Description
Our investigation is conducted in the domain of job shop scheduling. It is shown that, in this domain, generic CSP heuristics are usually not sufficient to guide the search for a feasible solution. This is because these heuristics fail to properly account for the tightness of constraints and/or the connectivity of the constraint graph. Instead, a probabilistic model of the search space is used to define new heuristics, which better account for these problem characteristics. Experimental results indicate that these new heuristics yield important improvements in both search efficiency and search time."
Author: Norman Sadeh Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 0
Book Description
Our investigation is conducted in the domain of job shop scheduling. It is shown that, in this domain, generic CSP heuristics are usually not sufficient to guide the search for a feasible solution. This is because these heuristics fail to properly account for the tightness of constraints and/or the connectivity of the constraint graph. Instead, a probabilistic model of the search space is used to define new heuristics, which better account for these problem characteristics. Experimental results indicate that these new heuristics yield important improvements in both search efficiency and search time."
Author: Bahareh Jafari Jashmi Publisher: ISBN: Category : Constraint programming (Computer science) Languages : en Pages : 140
Book Description
Constraint Satisfaction Problems (CSP) constitute a large number of problems in Artificial Intelligence (AI) and other areas of computer science. The search space of CSPs is either explored with systematic algorithms such as backtracking or with some form of local search which is non-systematic and incomplete. One important factor, that can reduce the size of the search space drastically, is the order in which the variables are examined. Many static and dynamic heuristics have been proposed for this purpose. Recent research shows that the heuristics that gather information about the failures during the constraint propagation phase in the form of constraint weights, for deciding variable ordering, are very powerful. These heuristics are called conflict driven heuristics. In this thesis, I introduce three methods that use non-systematic algorithms for weighing constraints. The first one uses Hill Climbing local search (HC), the second one uses Genetic Algorithm (GA), and the last one relies on Ant Colony Optimization (ACO). In addition, I propose two new value ordering techniques. The first one ranks the domain values of variables through a HC local search process and the second one does it through an ACO. Several experimentations were conducted on various types of problems including random, quasi random, patterned and real world problems. The experimentation results show that the proposed variable ordering approaches, particularly the ones that use ACO and HC, are successful in the case of hard random problems and some structured problems. However, conflict driven heuristics are the most successful in the case of real world and most of the structured problems. Using the proposed value ordering along with the proposed variable orderings can improve the performance, particularly in the case of random problems.
Author: Ket Wei Yong Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
A Constraint Satisfaction Problem (CSP) is a very powerful framework for representing and solving constraint problems. Many real world computational problems in Artificial Intelligence and other areas of computer science can be formulated as CSPs. Problems such as scheduling and timetabling in operations research, map-coloring problem and Boolean satisfiability are some of the examples that can be represented and solved with a CSP framework. Solving a CSP is about searching for a solution in a huge search space. Very often, much search efforts are wasted on the part of the search space that does not lead to a solution. Therefore many search algorithms and heuristic techniques have been proposed to solve CSPs efficiently by reducing the search space. Variable and Value Ordering is one of them. Many experiments and analyses have been conducted to show that good ordering of variables and values can significantly reduce the size of the search space and thus make the search more efficient. Many heuristics have been proposed for ordering variables or values. One such heuristics works by gathering information during search to guide subsequent decision in selecting variables. The heuristic gathers and records information about failures in the form of constraint weight during constraint propagation. Constraints will be assigned weights based on the information gathered. Each variable in the constraint graph will have a weighted degree which is the sum of the weights of the constraints the variable is involved in. In this thesis I will propose a variant of this heuristic where the weight of a constraint is also based on the conflict and support counts of each variable attached to this constraint. The conflict and support counts information is gathered during constraint propagation. The weight of the constraint is the ratio of conflict to support counts. I will also propose a dynamic value ordering heuristic based on the support and conflict count information. Experiments have been conducted on the proposed heuristics using the renowned benchmarks which include random, quasi-random, pattern and real world instances. The test results show that the proposed variable ordering heuristic perform well in the cases of hard random and quasi-random instances. The test results also show that combining the proposed variable and value ordering heuristics can improve the performance significantly in some difficult problems.
Author: Stuart Russell Publisher: Createspace Independent Publishing Platform ISBN: 9781537600314 Category : Languages : en Pages : 626
Book Description
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Author: Khaled Ghedira Publisher: John Wiley & Sons ISBN: 1118575016 Category : Mathematics Languages : en Pages : 245
Book Description
A Constraint Satisfaction Problem (CSP) consists of a set of variables, a domain of values for each variable and a set of constraints. The objective is to assign a value for each variable such that all constraints are satisfied. CSPs continue to receive increased attention because of both their high complexity and their omnipresence in academic, industrial and even real-life problems. This is why they are the subject of intense research in both artificial intelligence and operations research. This book introduces the classic CSP and details several extensions/improvements of both formalisms and techniques in order to tackle a large variety of problems. Consistency, flexible, dynamic, distributed and learning aspects are discussed and illustrated using simple examples such as the n-queen problem. Contents 1. Foundations of CSP. 2. Consistency Reinforcement Techniques. 3. CSP Solving Algorithms. 4. Search Heuristics. 5. Learning Techniques. 6. Maximal Constraint Satisfaction Problems. 7. Constraint Satisfaction and Optimization Problems. 8. Distibuted Constraint Satisfaction Problems. About the Authors Khaled Ghedira is the general managing director of the Tunis Science City in Tunisia, Professor at the University of Tunis, as well as the founding president of the Tunisian Association of Artificial Intelligence and the founding director of the SOIE research laboratory. His research areas include MAS, CSP, transport and production logistics, metaheuristics and security in M/E-government. He has led several national and international research projects, supervised 30 PhD theses and more than 50 Master’s theses, co-authored about 300 journal, conference and book research papers, written two text books on metaheuristics and production logistics and co-authored three others.
Author: Holger H. Hoos Publisher: Morgan Kaufmann ISBN: 1558608729 Category : Business & Economics Languages : en Pages : 678
Book Description
Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems. Offering a systematic treatment of SLS algorithms, this book examines the general concepts and specific instances of SLS algorithms and considers their development, analysis and application.
Author: Francesca Rossi Publisher: Elsevier ISBN: 0080463800 Category : Computers Languages : en Pages : 977
Book Description
Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.- Covers the whole field of constraint programming- Survey-style chapters- Five chapters on applications
Author: Robin Alexander Moser Publisher: Logos Verlag Berlin GmbH ISBN: 3832533699 Category : Computers Languages : en Pages : 215
Book Description
The Boolean satisfiability problem (SAT) and its generalization to variables of higher arities - constraint satisfaction problems (CSP) - can arguably be called the most "natural" of all NP-complete problems. The present work is concerned with their algorithmic treatment. It consists of two parts. The first part investigates CSPs for which satisfiability follows from the famous Lovasz Local Lemma. Since its discovery in 1975 by Paul Erdos and Laszlo Lovasz, it has been known that CSPs without dense spots of interdependent constraints always admit a satisfying assignment. However, an iterative procedure to discover such an assignment was not available. We refine earlier attempts at making the Local Lemma algorithmic and present a polynomial time algorithm which is able to make almost all known applications constructive. In the second part, we leave behind the class of polynomial time tractable problems and instead investigate the randomized exponential time algorithm devised and analyzed by Uwe Schoning in 1999, which solves arbitrary clause satisfaction problems. Besides some new interesting perspectives on the algorithm, the main contribution of this part consists of a refinement of earlier approaches at derandomizing Schoning's algorithm. We present a deterministic variant which losslessly reaches the performance of the randomized original.
Author: Norman Sadeh
Author: Norman Sadeh
Author: Norman Sadeh
Author: Bahareh Jafari Jashmi
Author: Ket Wei Yong
Author: Stuart Russell
Author: Khaled Ghedira
Author: Holger H. Hoos
Author: Francesca Rossi
Author: Ian P. Gent
Author: Robin Alexander Moser