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A one-stop reference, self-contained, with theoretical topics presented in conjunction with implementations for which code is supplied.
Mathematicians at every level use diagrams to prove theorems. Mathematical Reasoning with Diagrams investigates the possibilities of mechanizing this sort of diagrammatic reasoning in a formal computer proof system, even offering a semi-automatic formal proof system—called Diamond—which allows users to prove arithmetical theorems using diagrams.
Rippling is a radically new technique for the automation of mathematical reasoning. It is widely applicable whenever a goal is to be proved from one or more syntactically similar givens. It was originally developed for inductive proofs, where the goal was the induction conclusion and the givens were the induction hypotheses. It has proved to be applicable to a much wider class of tasks, from summing series via analysis to general equational reasoning. The application to induction has especially important practical implications in the building of dependable IT systems, and provides solutions to issues such as the problem of combinatorial explosion. Rippling is the first of many new search control techniques based on formula annotation; some additional annotated reasoning techniques are also described here. This systematic and comprehensive introduction to rippling, and to the wider subject of automated inductive theorem proving, will be welcomed by researchers and graduate students alike.
Handbook of Automated Reasoning
Learn about the latest in cognitive and autonomous network management Towards Cognitive Autonomous Networks: Network Management Automation for 5G and Beyond delivers a comprehensive understanding of the current state-of-the-art in cognitive and autonomous network operation. Authors Mwanje and Bell fully describe todays capabilities while explaining the future potential of these powerful technologies. This book advocates for autonomy in new 5G networks, arguing that the virtualization of network functions render autonomy an absolute necessity. Following that, the authors move on to comprehensively explain the background and history of large networks, and how we come to find ourselves in the place were in now. Towards Cognitive Autonomous Networks describes several novel techniques and applications of cognition and autonomy required for end-to-end cognition including: • Configuration of autonomous networks • Operation of autonomous networks • Optimization of autonomous networks • Self-healing autonomous networks The book concludes with an examination of the extensive challenges facing completely autonomous networks now and in the future.
This book presents some of the insights, judgements, opinions, and experiences gleaned from more than 30 years of research in automated reasoning. The style and organization are those of an experimenter's notebook, featuring both successes and failures resulting from numerous experiments with one of the world's most powerful software packages for automated reasoning, Bill McCune's OTTER.
To 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 bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions to problems that span many areas of the commonsense world. - Covers key areas of commonsense reasoning including action, change, defaults, space, and mental states. - The first full book on commonsense reasoning to use the event calculus. - Contextualizes the event calculus within the framework of commonsense reasoning, introducing the event calculus as the best method overall. - Focuses on how to use the event calculus formalism to perform commonsense reasoning, while existing papers and books examine the formalisms themselves. - Includes fully worked out proofs and circumscriptions for every example.
This book introduces SpecDB, an intelligent database created to represent and host software specifications in a machine-readable format, based on the principles of artificial intelligence and unit testing database operations. SpecDB is demonstrated via two automated intelligent tools. The first automatically generates database constraints from a rule-base in SpecDB. The second is a reverse engineering tool that logs the actual execution of the program from the code.
Reasoning with incomplete information constitutes a major challenge for any intelligent system. In fact, we expect such systems not to become paralyzed by missing information but rather to arrive at plausible results by bridging the gaps in the information available. A versatile way of reasoning in the absence of information is to reason by default. This book aims at providing formal and practical means for automating reasoning with incomplete information by starting from the approach taken by the framework of default logic. For this endeavor, a bridge is spanned between formal semantics, over systems for default reasoning, to efficient implementation.
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. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily