05470nam 22006614a 450 991078464950332120230120004708.01-281-05049-097866110504980-08-047661-9(CKB)1000000000364179(EBL)294040(OCoLC)703863158(SSID)ssj0000292062(PQKBManifestationID)11228970(PQKBTitleCode)TC0000292062(PQKBWorkID)10255946(PQKB)10971465(Au-PeEL)EBL294040(CaPaEBR)ebr10186070(CaSebORM)9780123693884(MiAaPQ)EBC294040(EXLCZ)99100000000036417920051103d2006 uy 0engur|n|---|||||txtccrCommonsense reasoning[electronic resource] /Erik T. Mueller1st editionAmsterdam ;Boston Elsevier Morgan Kaufmannc20061 online resource (431 p.)Description based upon print version of record.0-12-369388-8 Includes bibliographical references (p. 361-390) and index.Front cover; About the Author; Title page; Copyright page; Table of contents; Foreword; Preface; Why Commonsense Reasoning?; Approach; Intended Audience; Roadmap; Material Covered; Supplemental Materials; Web Site and Reasoning Programs; Exercises and Solutions; Text and Figure Acknowledgments; Acknowledgments; 1 Introduction; What Is Commonsense Reasoning?; Key Issues of Commonsense Reasoning; Summary; Brief History of Commonsense Reasoning; Logical Methods; Nonlogical Methods; The Event Calculus; Events, Fluents, and Timepoints; A Simple Example; Automated Event Calculus ReasoningBibliographic NotesPart I Foundations; 2 The Event Calculus; First-Order Logic; Syntax of First-Order Logic; Semantics of First-Order Logic; Proof Theory; Many-Sorted First-Order Logic; Notational Conventions; Event Calculus Basics; Event Calculus Sorts; Event Calculus Predicates; States of a Fluent; Event Calculus Axiomatizations; The (Continuous) Event Calculus; The Discrete Event Calculus; Choosing between the Event Calculus and the Discrete Event Calculus; Reification; Unique Names Axioms; Conditions; Circumscription; Computing Circumscription; Example: Circumscription of HappensExample: Circumscription of InitiatesDomain Descriptions; Example: Sleep; Inconsistency; Reasoning Types; Deduction and Temporal Projection; Abduction and Planning; Example: Sleep Abduction; Postdiction; Model Finding; Bibliographic Notes; Exercises; Part II Commonsense Phenomena; 3 The Effects of Events; Positive and Negative Effect Axioms; Example: Telephone; Effect Axiom Idioms; Preconditions; Fluent Preconditions; Action Preconditions; Example: Walking through a Door; State Constraints; Example: Telephone Revisited; Bibliographic Notes; Exercises; 4 The Triggering of EventsTrigger AxiomsExample: Alarm Clock; Preventing Repeated Triggering; Example: Bank Account Service Fee; Triggered Fluents; Bibliographic Notes; Exercises; 5 The Commonsense Law of Inertia; Representation of the Commonsense Law of Inertia; Frame Problem; Classical Frame Axioms; Explanation Closure Axioms; Minimizing Event Occurrences; Introduction of Initiates Predicate; Minimizing Event Effects; Introduction of Terminates Predicate; Discussion; Representing Release from the Commonsense Law of Inertia; Example: Yale Shooting Scenario; Releasing from Inertia; Restoring InertiaExplanation Closure Axioms for ReleasedAtExample: Russian Turkey Scenario; Release Axioms; Bibliographic Notes; Exercises; 6 Indirect Effects of Events; Effect Axioms; Example: Carrying a Book; Discussion; Primitive and Derived Fluents; Example: Device; Release Axioms and State Constraints; Example: Carrying a Book Revisited; Effect Constraints; Example: Carrying a Book Revisited; Causal Constraints; Example: Thielscher's Circuit; Trigger Axioms; Example: Thielscher's Circuit with Delays; Example: Shanahan's Circuit with Delays; Bibliographic Notes; Exercises; 7 Continuous ChangeTrajectory AxiomsTo endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringingCommonsense reasoningAutomationArtificial intelligenceMathematicsLogic, Symbolic and mathematicalData processingCommonsense reasoningAutomation.Artificial intelligenceMathematics.Logic, Symbolic and mathematicalData processing.153.4/3Mueller Erik T1497121MiAaPQMiAaPQMiAaPQBOOK9910784649503321Commonsense reasoning3722164UNINA05144nam 22007215 450 991029975510332120251117071851.01-4471-5571-810.1007/978-1-4471-5571-3(CKB)3710000000078699(DE-He213)978-1-4471-5571-3(SSID)ssj0001089970(PQKBManifestationID)11581019(PQKBTitleCode)TC0001089970(PQKBWorkID)11126777(PQKB)11051140(MiAaPQ)EBC6312757(MiAaPQ)EBC1591892(Au-PeEL)EBL1591892(CaPaEBR)ebr10965897(OCoLC)869904552(PPN)176097171(EXLCZ)99371000000007869920131206d2014 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierNeural Networks and Statistical Learning /by Ke-Lin Du, M. N. S. Swamy1st ed. 2014.London :Springer London :Imprint: Springer,2014.1 online resource (XXVII, 824 p. 166 illus., 68 illus. in color.)Bibliographic Level Mode of Issuance: Monograph1-4471-5570-X Introduction -- Fundamentals of Machine Learning -- Perceptrons -- Multilayer perceptrons: architecture and error backpropagation -- Multilayer perceptrons: other learning techniques -- Hopfield networks, simulated annealing and chaotic neural networks -- Associative memory networks -- Clustering I: Basic clustering models and algorithms -- Clustering II: topics in clustering -- Radial basis function networks -- Recurrent neural networks -- Principal component analysis -- Nonnegative matrix factorization and compressed sensing -- Independent component analysis -- Discriminant analysis -- Support vector machines -- Other kernel methods -- Reinforcement learning -- Probabilistic and Bayesian networks -- Combining multiple learners: data fusion and ensemble learning -- Introduction of fuzzy sets and logic -- Neurofuzzy systems -- Neural circuits -- Pattern recognition for biometrics and bioinformatics -- Data mining.Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.Computational intelligenceNeural networks (Computer science)Data miningPattern perceptionComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Mathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XComputational intelligence.Neural networks (Computer science)Data mining.Pattern perception.Computational Intelligence.Mathematical Models of Cognitive Processes and Neural Networks.Data Mining and Knowledge Discovery.Pattern Recognition.006.32Du Ke-Linauthttp://id.loc.gov/vocabulary/relators/aut756075Swamy M. N. S.authttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299755103321Neural Networks and Statistical Learning2041918UNINA