LEADER 00969cam0 22002773 450 001 SOB021253 005 20210506065149.0 010 $a8886858868 100 $a20040211d2001 |||||ita|0103 ba 101 $aita 102 $aIT 200 1 $aNon accettarmi come sono$eEsperienze didattiche d'integrazione delle persone diversamente abili nella scuola dell'autonomia$fMario Iacomino 210 $aAzzano San Paolo [BG]$cEdizioni Junior$d2001 215 $a553 p.$d24 cm 225 2 $aIntegrazione 410 1$1001LAEC00018876$12001 $a*Integrazione 700 1$aIacomino$b, Mario$3AF00013194$4070$0482810 801 0$aIT$bUNISOB$c20210506$gRICA 850 $aUNISOB 852 $aUNISOB$j370$m115614 912 $aSOB021253 940 $aM 102 Monografia moderna SBN 941 $aM 957 $a370$b001847$gSI$d115614$rACQUISTO$1Spinosa$2UNISOB$3UNISOB$420190524085538.0$520190524085552.0$6Spinosa 996 $aNon accettarmi come sono$91677098 997 $aUNISOB LEADER 05656nam 2200745 a 450 001 9910968154603321 005 20200520144314.0 010 $a9786611144944 010 $a9781281144942 010 $a1281144940 010 $a9780080557021 010 $a0080557023 035 $a(CKB)1000000000398649 035 $a(EBL)330132 035 $a(OCoLC)437198396 035 $a(SSID)ssj0000167755 035 $a(PQKBManifestationID)11169568 035 $a(PQKBTitleCode)TC0000167755 035 $a(PQKBWorkID)10177792 035 $a(PQKB)10526799 035 $a(MiAaPQ)EBC330132 035 $a(CaSebORM)9780444522115 035 $a(PPN)170265633 035 $a(OCoLC)824140469 035 $a(OCoLC)ocn824140469 035 $a(FR-PaCSA)45002505 035 $a(FRCYB45002505)45002505 035 $a(EXLCZ)991000000000398649 100 $a20080816d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aHandbook of knowledge representation /$fedited by Bruce Porter, Vladimir Lifschitz and Frank van Harmelen 205 $a1st ed. 210 $aAmsterdam $cElsevier$d2008 215 $a1 online resource (1035 p.) 225 1 $aFoundations of artificial intelligence 300 $aDescription based upon print version of record. 311 08$a9780444522115 311 08$a0444522115 320 $aIncludes bibliographical references and indexes. 327 $aFront cover; Handbook of Knowledge Representation; Copyright page; Dedication; Preface; Editors; Contributors; Contents; Part I: General Methods in Knowledge Representation and Reasoning; Chapter 1. Knowledge Representation and Classical Logic; 1.1 Knowledge Representation and Classical Logic; 1.2 Syntax, Semantics and Natural Deduction; 1.3 Automated Theorem Proving; 1.4 Applications of Automated Theorem Provers; 1.5 Suitability of Logic for Knowledge Representation; Acknowledgements; Bibliography; Chapter 2. Satisfiability Solvers; 2.1 Definitions and Notation 327 $a2.2 SAT Solver Technology-Complete Methods2.3 SAT Solver Technology-Incomplete Methods; 2.4 Runtime Variance and Problem Structure; 2.5 Beyond SAT: Quantified Boolean Formulas and Model Counting; Bibliography; Chapter 3. Description Logics; 3.1 Introduction; 3.2 A Basic DL and its Extensions; 3.3 Relationships with other Formalisms; 3.4 Tableau Based Reasoning Techniques; 3.5 Complexity; 3.6 Other Reasoning Techniques; 3.7 DLs in Ontology Language Applications; 3.8 Further Reading; Bibliography; Chapter 4. Constraint Programming; 4.1 Introduction; 4.2 Constraint Propagation; 4.3 Search 327 $a4.4 Tractability4.5 Modeling; 4.6 Soft Constraints and Optimization; 4.7 Constraint Logic Programming; 4.8 Beyond Finite Domains; 4.9 Distributed Constraint Programming; 4.10 Application Areas; 4.11 Conclusions; Bibliography; Chapter 5. Conceptual Graphs; 5.1 From Existential Graphs to Conceptual Graphs; 5.2 Common Logic; 5.3 Reasoning with Graphs; 5.4 Propositions, Situations, and Metalanguage; 5.5 Research Extensions; Bibliography; Chapter 6. Nonmonotonic Reasoning; 6.1 Introduction; 6.2 Default Logic; 6.3 Autoepistemic Logic; 6.4 Circumscription; 6.5 Nonmonotonic Inference Relations 327 $a6.6 Further Issues and ConclusionAcknowledgements; Bibliography; Chapter 7. Answer Sets; 7.1 Introduction; 7.2 Syntax and Semantics of Answer Set Prolog; 7.3 Properties of Logic Programs; 7.4 A Simple Knowledge Base; 7.5 Reasoning in Dynamic Domains; 7.6 Extensions of Answer Set Prolog; 7.7 Conclusion; Acknowledgements; Bibliography; Chapter 8. Belief Revision; 8.1 Introduction; 8.2 Preliminaries; 8.3 The AGM Paradigm; 8.4 Belief Base Change; 8.5 Multiple Belief Change; 8.6 Iterated Revision; 8.7 Non-Prioritized Revision; 8.8 Belief Update; 8.9 Conclusion; Acknowledgements; Bibliography 327 $aChapter 9. Qualitative Modeling9.1 Introduction; 9.2 Qualitative Mathematics; 9.3 Ontology; 9.4 Causality; 9.5 Compositional Modeling; 9.6 Qualitative States and Qualitative Simulation; 9.7 Qualitative Spatial Reasoning; 9.8 Qualitative Modeling Applications; 9.9 Frontiers and Resources; Bibliography; Chapter 10. Model-based Problem Solving; 10.1 Introduction; 10.2 Tasks; 10.3 Requirements on Modeling; 10.4 Diagnosis; 10.5 Test and Measurement Proposal, Diagnosability Analysis; 10.6 Remedy Proposal; 10.7 Other Tasks; 10.8 State and Challenges; Acknowledgements; Bibliography 327 $aChapter 11. Bayesian Networks 330 $aKnowledge Representation, which lies at the core of Artificial Intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field.This book is an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence.* Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve yo 410 0$aFoundations of artificial intelligence. 606 $aKnowledge representation (Information theory) 615 0$aKnowledge representation (Information theory) 676 $a006.332 676 $a006.332 701 $aPorter$b Bruce$f1956-$01796065 701 $aLifschitz$b Vladimir$0468243 701 $aVan Harmelen$b Frank$052740 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910968154603321 996 $aHandbook of knowledge representation$94337642 997 $aUNINA