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 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
This book constitutes the refereed proceedings of the 7th International Joint Conference on Automated Reasoning, IJCAR 2014, held as part of the Vienna Summer of Logic, VSL 2014, in Vienna, Austria, in July 2014. IJCAR 2014 was a merger of three leading events in automated reasoning, namely CADE (International Conference on Automated Deduction), FroCoS (International Symposium on Frontiers of Combining Systems) and TABLEAUX (International Conference on Automated Reasoning with Analytic Tableaux and Related Methods). The 26 revised full research papers and 11 system descriptions presented together with 3 invited talks were carefully reviewed and selected from 83 submissions. The papers have been organized in topical sections on HOL, SAT and QBF, SMT, equational reasoning, verification, proof theory, modal and temporal reasoning, SMT and SAT, modal logic, complexity, description logics and knowledge representation and reasoning.
This book constitutes the refereed proceedings of the 9th International Symposium on Functional and Logic Programming, FLOPS 2008. The 20 revised full papers, together with 3 invited contributions were carefully reviewed and selected from 59 submissions.
Artificial intelligence (AI) is a branch of computer science that models the human ability of reasoning, usage of human language and organization of knowledge, solving problems and practically all other human intellectual abilities. Usually it is charact- ized by the application of heuristic methods because in the majority of cases there is no exact solution to this kind of problem. The Mexican International Conference on Artificial Intelligence (MICAI), a yearly international conference series organized by the Mexican Society for Artificial Int- ligence (SMIA), is a major international AI forum and the main event in the academic life of the country’s growing AI community. In 2010, SMIA celebrated 10 years of activity related to the organization of MICAI as is represented in its slogan: “Ten years on the road with AI”. MICAI conferences traditionally publish high-quality papers in all areas of arti- cial intelligence and its applications. The proceedings of the previous MICAI events were also published by Springer in its Lecture Notes in Artificial Intelligence (LNAI) series, vols. 1793, 2313, 2972, 3789, 4293, 4827, 5317, and 5845. Since its foun- tion in 2000, the conference has been growing in popularity and improving in quality.
التأثير, الكتاب الكلاسيكي حول عملية الإقناع, يشرح مبادئ علم النفس التي تقف وراء قول الناس «نعم» لأي طلب كان, كما يشرح كيفية تطبيق فهم هذه المبادئ. الدكتور روبرت سيالديني مدرّس خبير في حقلي التأثير والإقناع المتناميين بسرعة. إن هذا الكتاب الذي ينظر إليه في الأوساط العلمية نظرة مرموقة جداً؛ فهو حصيلة خمس وثلاثين سنة من الأبحاث الدقيقة المعتمدة على الأدلة القوية، إضافة إلى برنامج استمر ثلاث سنوات من الدراسة الميدانية لما يدفع الناس إلى تغيير سلوكهم. سوف نتعلم المبادئ الستة الأساسية, وكيف نستعملها كي نصبح مهرة في فن الإقناع- وكيف ندافع عن أنفسنا ضد من يحاول أن يستغلنا بإقناعنا بما يريد. هذا الكتاب مثالي لجميع الناس العاملين في جميع نواحي الحياة. سوف تغيّر مبادئ التأثيرشخصيتك تغييراً إيجابياً قوياً وتقودك نحو النجاح. العبيكان للنشر