Knowledge Reasoning And Planning In Artificial Intelligence
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| Author | : Tri Duc Ta |
| Publisher | : Xoffencerpublication |
| Total Pages | : 220 |
| Release | : 2023-03-16 |
| Genre | : Business & Economics |
| ISBN | : 9394707573 |
AI, which stands for "artificial intelligence," is a discipline of computer science that focuses on providing machines the capacity to solve complex problems in a way that is more akin to how humans go about doing it. In most instances, this involves taking aspects of human intelligence and implementing them as algorithms in a format that is accessible to computers. manner. It is possible to pick a strategy that is either more or less flexible or efficient depending on the requirements that are described, and the degree to which the intelligent behaviour seems artificial is directly proportional to the strategy that is selected. AI is most commonly associated with the field of computer science; however, it has many significant connections to other fields, including Mathematics, Psychology, Cognition, Biology, and Philosophy, amongst a great number of others. This is because AI seeks to model human behaviour and thought processes in computer systems. How far we go in our quest of constructing an artificial intelligence will ultimately be determined by the degree to which we are able to combine our knowledge obtained from each of these subfields. At the moment, artificial intelligence encompasses a vast number of subfields, ranging from general-purpose areas such as perception and logical reasoning to specific tasks such as playing chess, proving mathematical theorems, writing poetry, and diagnosing diseases. Some examples of these more specific tasks include: playing chess, writing poetry, and diagnosing diseases. Some examples of these more specific tasks include: playing chess, writing poetry, diagnosing diseases, and so on. Scientists who have been working on intellectual projects their whole lives often make the transition gradually into artificial intelligence, where they discover the tools and terminology necessary to organize and automate the work they have been doing their entire careers. Workers in artificial intelligence have the option of applying their approaches to any field in which humans engage in intellectual activity. Because of this, we may confidently call it a universal field.
| Author | : Michael Gelfond |
| Publisher | : Cambridge University Press |
| Total Pages | : 363 |
| Release | : 2014-03-10 |
| Genre | : Computers |
| ISBN | : 1107782872 |
Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.
| Author | : Ronald Brachman |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 414 |
| Release | : 2004-05-19 |
| Genre | : Computers |
| ISBN | : 1558609326 |
Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.
| Author | : Frank van Harmelen |
| Publisher | : Elsevier |
| Total Pages | : 1035 |
| Release | : 2008-01-08 |
| Genre | : Computers |
| ISBN | : 0080557023 |
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
| Author | : Nils J. Nilsson |
| Publisher | : Elsevier |
| Total Pages | : 536 |
| Release | : 1998-04-17 |
| Genre | : Computers |
| ISBN | : 0080948340 |
Intelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches. - An evolutionary approach provides a unifying theme - Thorough coverage of important AI ideas, old and new - Frequent use of examples and illustrative diagrams - Extensive coverage of machine learning methods throughout the text - Citations to over 500 references - Comprehensive index
| Author | : Chitta Baral |
| Publisher | : Cambridge University Press |
| Total Pages | : 546 |
| Release | : 2003-01-09 |
| Genre | : Computers |
| ISBN | : 1139436449 |
Baral shows how to write programs that behave intelligently, by giving them the ability to express knowledge and to reason. This book will appeal to practising and would-be knowledge engineers wishing to learn more about the subject in courses or through self-teaching.
| Author | : Stuart Russell |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 626 |
| Release | : 2016-09-10 |
| Genre | : |
| ISBN | : 9781537600314 |
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
| Author | : Jack Minker |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 640 |
| Release | : 2000-12-31 |
| Genre | : Computers |
| ISBN | : 9780792372240 |
The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.
| Author | : David Heckerman |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 554 |
| Release | : 2014-05-12 |
| Genre | : Computers |
| ISBN | : 1483214516 |
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.
| Author | : Malik Ghallab |
| Publisher | : Cambridge University Press |
| Total Pages | : 373 |
| Release | : 2016-08-09 |
| Genre | : Computers |
| ISBN | : 1107037271 |
This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.
