The central theme of this collection is the epistemological status of constraints and preferences in linguistics. The contributions focus mainly on phonology; one article deals explicitly with morphology. The approaches to phonology represented in the volume are those of Natural Phonology, Government Phonology, Optimality Theory, autosegemental phonology, and computational phonology. Constraints are juxtaposed either to rules or to preferences in the discussion of constraint-based vs. preference-based theories.
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. In Constraint Processing, Rina Dechter synthesizes these contributions, as well as her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.
This book constitutes the thoroughly refereed post-proceedings of the Joint ERCIM/CologNet International Workshop on Constraint Solving and Constraint Logic Programming, held in Cork, Ireland in June 2002. The 14 revised full papers presented were carefully selected for inclusion in the book during two rounds of reviewing and revision. Among the topics addressed are verification and debugging of constraint logic programs, modeling and solving CSPs, explanation generation, inference and inconsistency processing, SAT and 0/1 encodings of CSPs, soft constraints and constraint relaxation, real-world applications, and distributed constraint solving.
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
This is the first international conference aimed at bringing the distributed database and distributed AI (DAD experts together, from both academia and industry, in order to discuss the issues of the next generation of knowledge based systems, namely Cooperating Knowledge Based Systems or CKBS for short. As the area of CKBS is new, we intended it to be an ideas conference - a conference where interesting new ideas, rather than results from completed projects, are explored, discussed, and debated. The conference was organised by the DAKE Centre. This is an interdisciplinary centre at the University of Keele for research and development in Data and Knowledge Engineering (DAKE). The Centre draws most of its strength from the Department of Computer Science which also provides administrative support for the activities of the Centre, although its membership is spread over several departments. The Centre has three main streams of research activities, namely: Large Knowledge Bases Software Engineering Neural Networks The Large Knowledge Base group, which provided the focus for this conference, is active in a number of research areas relating to data and knowledge bases, spanning from distributed databases to cooperations among data and knowledge bases. The current research topics include integration of data and knowledge bases and coopera ting knowledge based systems, with several major projects in the latter (see the entries under the Poster Session given below).
The 10th International Conference on the Principles and Practice of Constraint Programming (CP 2003) was held in Toronto, Canada, during September 27 – October 1, 2004. Information about the conference can be found on the Web at http://ai.uwaterloo.ca/~cp2004/ Constraint programming (CP) is about problem modelling, problem solving, programming, optimization, software engineering, databases, visualization, user interfaces, and anything to do with satisfying complex constraints. It reaches into mathematics, operations research, arti?cial intelligence, algorithms, c- plexity, modelling and programming languages, and many aspects of computer science. Moreover, CP is never far from applications, and its successful use in industry and government goes hand in hand with the success of the CP research community. Constraintprogrammingcontinuesto beanexciting,?ourishingandgrowing research?eld,astheannualCPconferenceproceedingsamplywitness.Thisyear, from 158 submissions, we chose 46 to be published in full in the proceedings. Instead of selecting one overall best paper, we picked out four “distinguished” papers – though we were tempted to select at least 12 such papers. In addition we included 16 short papersin the proceedings– these were presentedas posters at CP 2004. This volume includes summaries of the four invited talks of CP 2004. Two speakers from industry were invited. However these were no ordinary industrial representatives,buttwoofthe leadingresearchersinthe CPcommunity:Helmut Simonis of Parc Technologies, until its recent takeover by Cisco Systems; and Jean Francoi ̧ s Puget, Director of Optimization Technology at ILOG. The other two invited speakers are also big movers and shakers in the researchcommunity.
This book constitutes the refereed proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, CP 2007. It contains 51 revised full papers and 14 revised short papers presented together with eight application papers and the abstracts of two invited lectures. All current issues of computing with constraints are addressed, ranging from methodological and foundational aspects to solving real-world problems in various application fields.
Change is one of the most significant parameters in our society. Designers are amongst the primary change agents for any society. As a consequence design is an important research topic in engineering and architecture and related disciplines, since design is not only a means of change but is also one of the keystones to economic competitiveness and the fundamental precursor to manufacturing. The development of computational models founded on the artificial intelligence paradigm has provided an impetus for much of current design research -both computational and cognitive. These forms of design research have only been carried out in the last decade or so and in the temporal sense they are still immature. Notwithstanding this immaturity, noticeable advances have been made both in extending our understanding of design and in developing tools based on that understanding. Whilst many researchers in the field of artificial intelligence in design utilise ideas about how humans design as one source of concepts there is normally no attempt to model human designers. Rather the results of the research presented in this volume demonstrate approaches to increasing our understanding of design as a process.
What role, if any, does formal logic play in characterizing epistemically rational belief? Traditionally, belief is seen in a binary way - either one believes a proposition, or one doesn't. Given this picture, it is attractive to impose certain deductive constraints on rational belief: that one's beliefs be logically consistent, and that one believe the logical consequences of one's beliefs. A less popular picture sees belief as a graded phenomenon. This picture (explored more bydecision-theorists and philosophers of science thatn by mainstream epistemologists) invites the use of probabilistic coherence to constrain rational belief. But this latter project has often involved defining graded beliefs in terms of preferences, which may seem to change the subject away fromepistemic rationality.Putting Logic in its Place explores the relations between these two ways of seeing beliefs. It argues that the binary conception, although it fits nicely with much of our commonsense thought and talk about belief, cannot in the end support the traditional deductive constraints on rational belief. Binary beliefs that obeyed these constraints could not answer to anything like our intuitive notion of epistemic rationality, and would end up having to be divorced from central aspects of ourcognitive, practical, and emotional lives.But this does not mean that logic plays no role in rationality. Probabilistic coherence should be viewed as using standard logic to constrain rational graded belief. This probabilistic constraint helps explain the appeal of the traditional deductive constraints, and even underlies the force of rationally persuasive deductive arguments. Graded belief cannot be defined in terms of preferences. But probabilistic coherence may be defended without positing definitional connections between beliefsand preferences. Like the traditional deductive constraints, coherence is a logical ideal that humans cannot fully attain. Nevertheless, it furnishes a compelling way of understanding a key dimension of epistemic rationality.
