Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI.

This volume contains the papers presentedat the 20th InternationalConference onLogicProgramming, held in Saint-Malo, France, September 6-10,2004.Since the ?rst meeting in this series, held in Marseilles in 1982, ICLP has been the premier international conference for presenting research in logic programming. This year, we received 70 technical papers from countries all over the world, and the ProgramCommittee accepted 28 of them for presentation;they are included in this volume. A stand-by-your-postersession took place during the conference. It servedas aforumforpresentingworkinamoreinformalandinteractivesetting.Abstracts ofthe16postersselectedbytheProgramCommitteeareincludedinthisvolume as well. The conference program also included invited talks and invited tutorials. We were privileged to have talks by three outstanding researchers and excellent speakers: Nachum Dershowitz (Tel Aviv University, Israel) talked on Ter- nation by Abstraction, Michael Gelfond (Texas Tech University, USA) on - swer Set Programming and the Design of Deliberative Agents, andG erard Huet (INRIA, France) on Non-determinism Lessons. Two of the invited talks appear in these proceedings. The tutorials covered topics of high interest to the logic programming community: Ilkka Niemel] a gave a tutorial on The Implementation of Answer Set Solvers, Andreas PodelskionTree Automata in Program Analysis and Veri?cation, and Guillermo R. Simari onDefeasible Logic Programming and Belief Revision. Satelliteworkshopsmadetheconferenceevenmoreinteresting.Sixworkshops collocated with ICLP 2004: - CICLOPS2004, Colloquium on Implementation of Constraint and Logic Programming Systems, organized by Manuel Carro. - COLOPS2004,2ndInternationalWorkshoponConstraint&LogicProgr- ming in Security, organized by Frank Valencia. - MultiCPL2004, 3rd International Workshop on Multiparadigm Constraint, organized by Petra Hofstedt. - TeachLP2004,1stInternationalWorkshoponTeachingLogicProgramming, organized by Dietmar Seipel."

The papers in this collection were presented at the 7th International Con- rence on Logic Programming and Nonmonotonic Reasoning (LPNMR-7) in Fort Lauderdale, Florida, USA, during January 6-8, 2004. The previous meetings in this series were held in Washington, DC, USA (1991), Lisbon, Portugal (1993), Lexington, USA (1995), Dagstuhl, Germany (1997), El Paso, USA (1999), and Vienna, Austria (2001). LPNMR conferences are a forum for exchanging ideas on declarative logic programming, nonmonotonic reasoning and knowledge representation. In the 1980sresearchersworkingintheareaofnonmonotonicreasoningdiscoveredthat their formalisms could be used to describe the behavior of negation as failure in Prolog, andthe?rstLPNMRmeetingwasconvenedforthepurposeofdiscussing thisrelationship.Thisworkhasledtothecreationoflogicprogrammingsystems of a new kind, answer set solvers, and to the emergence of a new approach to solving combinatorial search problems, called answer set programming. The highlights of LPNMR-7 were three invited talks, given by Rina Dechter (University of California, Irvine), Henry Kautz (University of Washington) and Torsten Schaub (University of Potsdam). The program also included 24 regular papers selected after a rigorous review process, 8 system descriptions, and 2 panels. We would like to thank the Program Committee members and additional reviewers for careful, unbiased evaluation of the submitted papers. We are also grateful to Paolo Ferraris for help with publicizing the Call for Papers, to Fred Ho?man for help with local organizational matters, and to Matti J] arvisalo for help with the organization of the electronic Program Committee meeting."

Answer set programming (ASP) is a programming methodology oriented towards combinatorial search problems. In such a problem, the goal is to find a solution among a large but finite number of possibilities. The idea of ASP came from research on artificial intelligence and computational logic. ASP is a form of declarative programming: an ASP program describes what is counted as a solution to the problem, but does not specify an algorithm for solving it. Search is performed by sophisticated software systems called answer set solvers.Combinatorial search problems often arise in science and technology, and ASP has found applications in diverse areas-in historical linguistic, in bioinformatics, in robotics, in space exploration, in oil and gas industry, and many others. The importance of this programming method was recognized by the Association for the Advancement of Artificial Intelligence in 2016, when AI Magazine published a special issue on answer set programming. The book introduces the reader to the theory and practice of ASP. It describes the input language of the answer set solver CLINGO, which was designed at the University of Potsdam in Germany and is used today by ASP programmers in many countries. It includes numerous examples of ASP programs and present the mathematical theory that ASP is based on. There are many exercises with complete solutions.

Answer set programming (ASP) is a programming methodology oriented towards combinatorial search problems. In such a problem, the goal is to find a solution among a large but finite number of possibilities. The idea of ASP came from research on artificial intelligence and computational logic. ASP is a form of declarative programming: an ASP program describes what is counted as a solution to the problem, but does not specify an algorithm for solving it. Search is performed by sophisticated software systems called answer set solvers.Combinatorial search problems often arise in science and technology, and ASP has found applications in diverse areas-in historical linguistic, in bioinformatics, in robotics, in space exploration, in oil and gas industry, and many others. The importance of this programming method was recognized by the Association for the Advancement of Artificial Intelligence in 2016, when AI Magazine published a special issue on answer set programming. The book introduces the reader to the theory and practice of ASP. It describes the input language of the answer set solver CLINGO, which was designed at the University of Potsdam in Germany and is used today by ASP programmers in many countries. It includes numerous examples of ASP programs and present the mathematical theory that ASP is based on. There are many exercises with complete solutions.