Artificial Intelligence A New Synthesis

Author : Nils J. Nilsson
Publisher : Morgan Kaufmann
ISBN 13 : 1558605355
Total Pages : 536 pages
Book Rating : 4.50/5 ( download)

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Download or read book Artificial Intelligence written by Nils J. Nilsson and published by Morgan Kaufmann. This book was released on 1998 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book, by one of the most respected researchers in Artificial Intelligence, features a radical new 'evolutionary' organization that begins with low level intelligent behavior and develops complex intelligence as the book progresses.

Author : Nils J. Nilsson
Publisher : Elsevier
ISBN 13 : 0080948340
Total Pages : 513 pages
Book Rating : 4.48/5 ( download)

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Download or read book Artificial Intelligence: A New Synthesis written by Nils J. Nilsson and published by Elsevier. This book was released on 1998-04-17 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Nils J. Nilsson
Publisher : Morgan Kaufmann
ISBN 13 : 1558604677
Total Pages : 537 pages
Book Rating : 4.74/5 ( download)

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Download or read book Artificial Intelligence written by Nils J. Nilsson and published by Morgan Kaufmann. This book was released on 1998-04 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nilsson employs increasingly capable intelligent agents in an evolutionary approach--a novel perspective from which to view and teach topics in artificial intelligence.

Author : Nils J. Nilsson
Publisher : Elsevier
ISBN 13 : 0080499457
Total Pages : 605 pages
Book Rating : 4.51/5 ( download)

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Download or read book Artificial Intelligence written by Nils J. Nilsson and published by Elsevier. This book was released on 1998-04-17 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent agents are employed as the central characters in this new 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. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. 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 : Luc De Raedt
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627058427
Total Pages : 191 pages
Book Rating : 4.21/5 ( download)

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Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt and published by Morgan & Claypool Publishers. This book was released on 2016-03-24 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Author : Pedro Dechter
Publisher : Springer Nature
ISBN 13 : 3031015495
Total Pages : 145 pages
Book Rating : 4.96/5 ( download)

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Download or read book Markov Logic written by Pedro Dechter and published by Springer Nature. This book was released on 2022-05-31 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion

Author : Sergey I. Nikolenko
Publisher : Springer Nature
ISBN 13 : 3030751783
Total Pages : 348 pages
Book Rating : 4.84/5 ( download)

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Download or read book Synthetic Data for Deep Learning written by Sergey I. Nikolenko and published by Springer Nature. This book was released on 2021-06-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

Author : N. J. Nilsson
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.24/5 ( download)

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Download or read book Problem Solving Methods in Artificial Intelligence written by N. J. Nilsson and published by . This book was released on 1977 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Nathan Brown
Publisher : Royal Society of Chemistry
ISBN 13 : 1839160543
Total Pages : 425 pages
Book Rating : 4.47/5 ( download)

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Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Author : Burr Chen
Publisher : Springer Nature
ISBN 13 : 3031015606
Total Pages : 100 pages
Book Rating : 4.01/5 ( download)

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Download or read book Active Learning written by Burr Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations