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Constraint-based reasoning is an important area of automated reasoning in artificial intelligence, with many applications. These include configuration and design problems, planning and scheduling, temporal and spatial reasoning, defeasible and causal reasoning, machine vision and language understanding, qualitative and diagnostic reasoning, and expert systems. Constraint-Based Reasoning presents current work in the field at several levels: theory, algorithms, languages, applications, and hardware. Constraint-based reasoning has connections to a wide variety of fields, including formal logic, graph theory, relational databases, combinatorial algorithms, operations research, neural networks, truth maintenance, and logic programming. The ideal of describing a problem domain in natural, declarative terms and then letting general deductive mechanisms synthesize individual solutions has to some extent been realized, and even embodied, in programming languages. Contents Introduction, E. C. Freuder, A. K. Mackworth * The Logic of Constraint Satisfaction, A. K. Mackworth * Partial Constraint Satisfaction, E. C. Freuder, R. J. Wallace * Constraint Reasoning Based on Interval Arithmetic: The Tolerance Propagation Approach, E. Hyvonen * Constraint Satisfaction Using Constraint Logic Programming, P. Van Hentenryck, H. Simonis, M. Dincbas * Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems, S. Minton, M. D. Johnston, A. B. Philips, and P. Laird * Arc Consistency: Parallelism and Domain Dependence, P. R. Cooper, M. J. Swain * Structure Identification in Relational Data, R. Dechter, J. Pearl * Learning to Improve Constraint-Based Scheduling, M. Zweben, E. Davis, B. Daun, E. Drascher, M. Deale, M. Eskey * Reasoning about Qualitative Temporal Information, P. van Beek * A Geometric Constraint Engine, G. A. Kramer * A Theory of Conflict Resolution in Planning, Q. Yang A Bradford Book.
Much of AI research is about problem-solving strategies, and several techniques have been crystalized. One such technique is constraint satisfaction or reasoning based on relations. Constraint-based reasoning is used to solve a wide field of problems, and recently constraint techniques have been incorporated into logic programming languages, yielding a whole new field of research and application: constraint logic programming. Constraint satisfaction techniques have become part of almost all introductory books on AI. This monograph is about constraint satisfaction. It differs from others in that it presents all approaches under a common, generalizing view: dynamic constraints. This new way of viewing constraints provides new insights about the different approaches, and forms a very practical basis for teaching constraint-based reasoning. A uniform view of the constraint world is also a good basis for constraint research. This text is not intended to be a self-contained textbook on constraint-based reasoning, but rather a coherent text on an interesting view of the field.
Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.
"Much of AI research is about problem-solving strategies, and several techniques have been crystalized. One such technique is constraint satisfaction or reasoning based on relations. Constraint-based reasoning is used to solve a wide field of problems, and recently constraint techniques have been incorporated into logic programming languages, yielding a whole new field of research and application: constraint logic programming. Constraint satisfaction techniques have become part of almost all introductory books on AI. This monograph is about constraint satisfaction. It differs from others in that it presents all approaches under a common, generalizing view: dynamic constraints. This new way of viewing constraints provides new insights about the different approaches, and forms a very practical basis for teaching constraint-based reasoning. A uniform view of the constraint world is also a good basis for constraint research. This text is not intended to be a self-contained textbook on constraint-based reasoning, but rather a coherent text on an interesting view of the field."--PUBLISHER'S WEBSITE.
Constraint-Based Grammar Formalisms provides the first rigorous mathematical and computational basis for this important area.
The volume is based on the papers that were presented at the Interna tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de ductive and inductive arguments. Understanding the contribution of model ing practices to discovery and conceptual change in science requires ex panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model based reasoning to be considered in this book. The models are intended as in terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.
With the aim of automatically reasoning with spatial aspects in a cognitive way, several qualitative models have been developed recently in the Qualitative Spatial Reasoning field. However, there is no model to reason with several spatial aspects in a uniform way. Moreover, most of these models simplify spatial objects to points. In this book we present a novel approach for integrating the qualitative concepts of orientation, distance, and cardinal directions, using points as well as extended objects as primitive of reasoning, based on Constraint Logic Programming. The resulting model has been applied to build a qualitative Navigation Simulator on the structured environment of the city of Castellon.
Constraint programming is like an octopus spreading its tentacles into databases, operations research, artificial intelligence, and many other areas. The concept of constraint programming was introduced in artificial intelligence and graphics in the 1960s and 1970s. Now the related techniques are used and studied in many fields of computing. Different aspects of constraint processing are investigated in theoretical computer science, logic programming, knowledge representation, operations research, and related application domains. Constraint programming has been included in the lists of related topics of many conferences. Nevertheless, only in 1993 were the first forums held, devoted as a whole to this field of knowledge. These were the First Workshop on Principles and Practice of Constraint Programming (PPCP'93) which was held in Newport, Rhode Island, USA, April 28-30, the International Workshop on Constraint Processing (at CSAM'93) held in St. Petersburg, Russia, July 20-21, and the NATO Advanced Study Institute (NATO AS!) on Constraint Programming held in Parnu, Estonia, August 13-24. NATO A Sis are aimed to be schools bringing together leading researchers and practitioners from industry and academia in some area of knowledge to provide a concise picture of the work done and results obtained by different groups. This is intended for dissemination of advanced knowledge not yet taught regularly in of new topics university. However, ASis must also encourage the introduction into university curricula as well as foster international scientific contacts.
Foundations of Constraint Satisfaction discusses the foundations of constraint satisfaction and presents algorithms for solving constraint satisfaction problems (CSPs). Most of the algorithms described in this book are explained in pseudo code, and sometimes illustrated with Prolog codes (to illustrate how the algorithms could be implemented). Comprised of 10 chapters, this volume begins by defining the standard CSP and the important concepts around it and presenting examples and applications of CSPs. The reader is then introduced to the main features of CSPs and CSP solving techniques (problem reduction, searching, and solution synthesis); some of the most important concepts related to CSP solving; and problem reduction algorithms. Subsequent chapters deal with basic control strategies of searching which are relevant to CSP solving; the significance of ordering the variables, values and compatibility checking in searching; specialized search techniques which gain their efficiency by exploiting problem-specific features; and stochastic search approaches (including hill climbing and connectionist approaches) for CSP solving. The book also considers how solutions can be synthesized rather than searched for before concluding with an analysis of optimization in CSPs. This monograph can be used as a reference by artificial intelligence (AI) researchers or as a textbook by students on advanced AI courses, and should also help knowledge engineers apply existing techniques to solve CSPs or problems which embed CSPs.