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Constraint Processing
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Rina Dechter synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.Abstract:
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning.
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.
·Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges
·Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis
·Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics
Table of Contents
Preface; Introduction; Constraint Networks; Consistency-Enforcing Algorithms: Constraint Propagation; Directional Consistency; General Search Strategies; General Search Strategies: Look-Back; Local Search Algorithms; Advanced Consistency Methods; Tree-Decomposition Methods; Hybrid of Search and Inference: Time-Space Trade-offs; Tractable Constraint Languages; Temporal Constraint Networks; Constraint Optimization; Probabilistic Networks; Constraint Logic Programming; Bibliographyread more
Citations
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A new efficient and flexible algorithm for the design of testable subsystems
TL;DR: This paper presents a general method for finding all the possible testable subsystems, i.e., sets of relations that can lead to various types of detection tests, and provides the constraints that have to be used for the design of each detection test.
Proceedings ArticleDOI
iRDM: A solution for workforce supply chain management in an outsourcing environment
TL;DR: In this paper, an integrated solution developed by IBM Research division to support the full lifecycle management of highly-skilled professionals in the outsourcing environment, iRDM covers both the demand side and the supply side, and matching between demand and supply.
Proceedings Article
An interdisciplinary method for a generic vehicle routing problem decision support system
TL;DR: An interdisciplinary method for the logistics of transportation that allows to consider and to process the constraints identified by the work domain analysis during the problem resolution and is well adapted to the consideration of the Human as a main actor in the decision-making process.
Journal ArticleDOI
The implementation of a direct search approach for the resolution of complex and changing rule-based problems
TL;DR: An approach has been developed that employs sensitivity analysis and problem strategies to form an evolving direct search technique that has been applied to a range of engineering problems and is illustrated here through its use in a study into the posture modeling of humans.
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Obligation as Optimal Goal Satisfaction
Robert A. Kowalski,Ken Satoh +1 more
TL;DR: A non-modal approach to the treatment of deontic concepts deals with problems of contrary-to-duty obligations and normative conflicts, and it is argued that the approach is useful for many other applications, including abductive explanations, defeasible reasoning, combinatorial optimisation, and reactive systems of the production system variety.
References
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Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.