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Rule-based programming.

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The book presents logical foundations for rule-based systems, as seen by the Author. An attempt has been made to provide an in-depth discussion of logical and other aspects of such systems, including languages for knowledge representation, inference mechanisms, inference control, design and verification. The ultimate goal was to provide a deeper theoretical insight into the nature of rule-based systems and put together the most complete presentation including details so frequently skipped in typical textbooks. The main parts present material on: • logical foundations of rule-based systems (Part I); • principles of rule-based systems structures, knowledge representation languages, inference and inference control (Part II); • verification of formal properties of rule-based systems (Part III); • design methodology for efficient development of such systems (Part IV). The book may be useful to potentially wide audience, but it is aimed at providing specific knowledge for graduate, post-graduate and Ph.D. students, as well as knowledge engineers and research workers involved in the domain of AI. It also constitutes a summary of the Author’s research and experience gathered through several years of his research work.

Expert systems (Computer science) --- Automatic control. --- Systèmes experts (Informatique) --- Commande automatique --- Engineering. --- Artificial intelligence. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Rule-based programming --- Automatic control --- Civil Engineering --- Computer Science --- Applied Mathematics --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Control engineering --- Control equipment --- Knowledge-based systems (Computer science) --- Systems, Expert (Computer science) --- Applied mathematics. --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Artificial intelligence --- Computer systems --- Soft computing --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Rule-based programming. --- Rule-based methods (Computer science) --- Computer programming

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Reasoning about knowledge--particularly the knowledge of agents who reason about the world and each other's knowledge--was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms.Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes.

Philosophy --- Philosophy & Religion --- Speculative Philosophy --- Knowledge, Theory of --- Agent (Philosophy) --- Reasoning --- Argumentation --- Ratiocination --- Agency (Philosophy) --- Agents --- Person (Philosophy) --- Epistemology --- Theory of knowledge --- Reason --- Thought and thinking --- Judgment (Logic) --- Logic --- Act (Philosophy) --- Psychology --- Knowledge, Theory of. --- Reasoning. --- COMPUTER SCIENCE/General --- 681.3*I24 --- 681.3*I24 Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence)

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Modeling and implementing dynamical systems is a central problem in artificial intelligence, robotics, software agents, simulation, decision and control theory, and many other disciplines. In recent years, a new approach to representing such systems, grounded in mathematical logic, has been developed within the AI knowledge-representation community. This book presents a comprehensive treatment of these ideas, basing its theoretical and implementation foundations on the situation calculus, a dialect of first-order logic. Within this framework, it develops many features of dynamical systems modeling, including time, processes, concurrency, exogenous events, reactivity, sensing and knowledge, probabilistic uncertainty, and decision theory. It also describes and implements a new family of high-level programming languages suitable for writing control programs for dynamical systems. Finally, it includes situation calculus specifications for a wide range of examples drawn from cognitive robotics, planning, simulation, databases, and decision theory, together with all the implementation code for these examples. This code is available on the book's Web site.

Knowledge representation (Information theory) --- Expert systems (Computer science) --- Logic, Symbolic and mathematical --- Représentation des connaissances --- Systèmes experts (Informatique) --- Logique symbolique et mathématique --- Systèmes experts (informatique) --- Logique mathématique --- Logic, Symbolic and mathematical. --- 681.3*I24 --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Knowledge-based systems (Computer science) --- Systems, Expert (Computer science) --- Artificial intelligence --- Computer systems --- Soft computing --- Representation of knowledge (Information theory) --- Information theory --- Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Engineering & Applied Sciences --- Computer Science --- 681.3*I24 Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Représentation des connaissances. --- Logique mathématique. --- COMPUTER SCIENCE/Artificial Intelligence

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Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models