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Proceedings Of The Eighth International Joint Conference On Artificial Intelligence
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Book Synopsis Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) by : Sarit Kraus
Download or read book Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) written by Sarit Kraus and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the Ninth International Joint Conference on Artificial Intelligence by : International Joint Conferences on Artificial Intelligence
Download or read book Proceedings of the Ninth International Joint Conference on Artificial Intelligence written by International Joint Conferences on Artificial Intelligence and published by Elsevier. This book was released on 1985 with total page 1368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis AI 2016: Advances in Artificial Intelligence by : Byeong Ho Kang
Download or read book AI 2016: Advances in Artificial Intelligence written by Byeong Ho Kang and published by Springer. This book was released on 2016-11-25 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 29th Australasian Joint Conference on Artificial Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016. The 40 full papers and 18 short papers presented together with 8 invited short papers were carefully reviewed and selected from 121 submissions. The papers are organized in topical sections on agents and multiagent systems; AI applications and innovations; big data; constraint satisfaction, search and optimisation; knowledge representation and reasoning; machine learning and data mining; social intelligence; and text mining and NLP. The proceedings also contains 2 contributions of the AI 2016 doctoral consortium and 6 contributions of the SMA 2016.
Book Synopsis Artificial Intelligence for Knowledge Management by : Eunika Mercier-Laurent
Download or read book Artificial Intelligence for Knowledge Management written by Eunika Mercier-Laurent and published by Springer Nature. This book was released on 2021-07-03 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a selection of extended papers presented at the 8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021, held in Yokohama, Japan, in January 2021, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2020.* The 14 revised and extended papers presented together with an invited talk were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and discuss methodological, technical and organizational aspects of artificial intelligence used for knowledge management. *The workshop was held virtually.
Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Book Synopsis Proceedings of the ... International Joint Conference on Artificial Intelligence by :
Download or read book Proceedings of the ... International Joint Conference on Artificial Intelligence written by and published by . This book was released on 1999 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Eighth International Work-Conference on Artificial and Natural Neural Networks by : Joan Cabestany
Download or read book Eighth International Work-Conference on Artificial and Natural Neural Networks written by Joan Cabestany and published by Springer Science & Business Media. This book was released on 2005-05-30 with total page 1282 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present in this volume the collection of finally accepted papers of the eighth edition of the “IWANN” conference (“International Work-Conference on Artificial Neural Networks”). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). Since the first edition of IWANN in Granada (LNCS 540, 1991), the Artificial Neural Network (ANN) community, and the domain itself, have matured and evolved. Under the ANN banner we find a very heterogeneous scenario with a main interest and objective: to better understand nature and beings for the correct elaboration of theories, models and new algorithms. For scientists, engineers and professionals working in the area, this is a very good way to get solid and competitive applications. We are facing a real revolution with the emergence of embedded intelligence in many artificial systems (systems covering diverse fields: industry, domotics, leisure, healthcare, ... ). So we are convinced that an enormous amount of work must be, and should be, still done. Many pieces of the puzzle must be built and placed into their proper positions, offering us new and solid theories and models (necessary tools) for the application and praxis of these current paradigms. The above-mentioned concepts were the main reason for the subtitle of the IWANN 2005 edition: “Computational Intelligence and Bioinspired Systems.” The call for papers was launched several months ago, addressing the following topics: 1. Mathematical and theoretical methods in computational intelligence.
Book Synopsis Handbook of Knowledge Representation by : Frank van Harmelen
Download or read book Handbook of Knowledge Representation written by Frank van Harmelen and published by Elsevier. This book was released on 2008-01-08 with total page 1035 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
Book Synopsis Computational Models of Argument by : H. Prakken
Download or read book Computational Models of Argument written by H. Prakken and published by IOS Press. This book was released on 2020-09-25 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: The investigation of computational models of argument is a rich and fascinating interdisciplinary research field with two ultimate aims: the theoretical goal of understanding argumentation as a cognitive phenomenon by modeling it in computer programs, and the practical goal of supporting the development of computer-based systems able to engage in argumentation-related activities with human users or among themselves. The biennial International Conferences on Computational Models of Argument (COMMA) provide a dedicated forum for the presentation and discussion of the latest advancements in the field, and cover both basic research and innovative applications. This book presents the proceedings of COMMA 2020. Due to the Covid-19 pandemic, COMMA 2020 was held as an online event on the originally scheduled dates of 8 -11 September 2020, organised by the University of Perugia, Italy. The book includes 28 full papers and 13 short papers selected from a total of 78 submissions, the abstracts of 3 invited talks and 13 demonstration abstracts. The interdisciplinary nature of the field is reflected, and contributions cover both theory and practice. Theoretical contributions include new formal models, the study of formal or computational properties of models, designs for implemented systems and experimental research. Practical papers include applications to medicine, law and criminal investigation, chatbots and online product reviews. The argument-mining trend from previous COMMA’s is continued, while an emerging trend this year is the use of argumentation for explainable AI. The book provided an overview of the latest work on computational models of argument, and will be of interest to all those working in the field.
Book Synopsis 2019 International Conference on Artificial Intelligence and Advanced Manufacturing by :
Download or read book 2019 International Conference on Artificial Intelligence and Advanced Manufacturing written by and published by . This book was released on 2019 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Qualitative Reasoning about Physical Systems by : Daniel G Bobrow
Download or read book Qualitative Reasoning about Physical Systems written by Daniel G Bobrow and published by Elsevier. This book was released on 2012-12-02 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume brings together current work on qualitative reasoning. Its publication reflects the maturity of qualitative reasoning as a research area and the growing interest in problems of reasoning about physical systems.The papers present knowledge bases for a number of very different domains, including heat flow, transistors, and digital computation. A common theme of all these papers is explaining how physical systems work. An important shared criterion is that the behavioral description must be compositional, that is the description of a system's behavior must be derivable from the structure of the system.This material should be of interest to anyone concerned with automated reasoning about the real (physical) world.
Book Synopsis Machine Learning for Causal Inference by : Sheng Li
Download or read book Machine Learning for Causal Inference written by Sheng Li and published by Springer Nature. This book was released on 2023-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Book Synopsis An Introduction to Lifted Probabilistic Inference by : Guy Van den Broeck
Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
Download or read book SAT 2005 written by Enrico Giunchiglia and published by Springer Science & Business Media. This book was released on 2007-01-21 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers recent progress in solving propositional satisfiability and related problems. Propositional satisfiability is a powerful and general formalism used to solve a wide range of important problems including hardware and software verification. Research into methods to automate such reasoning has therefore a long history in artificial intelligence. This book follows on from the highly successful volume entitled SAT 2000 published five years ago.
Book Synopsis Elements of Machine Learning by : Pat Langley
Download or read book Elements of Machine Learning written by Pat Langley and published by Morgan Kaufmann. This book was released on 1996 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is the computational study of algorithms that improve performance based on experience, and this book covers the basic issues of artificial intelligence. Individual sections introduce the basic concepts and problems in machine learning, describe algorithms, discuss adaptions of the learning methods to more complex problem-solving tasks and much more.
Book Synopsis Distributed Artificial Intelligence by : Michael N. Huhns
Download or read book Distributed Artificial Intelligence written by Michael N. Huhns and published by Elsevier. This book was released on 2012-12-02 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Artificial Intelligence presents a collection of papers describing the state of research in distributed artificial intelligence (DAI). DAI is concerned with the cooperative solution of problems by a decentralized group of agents. The agents may range from simple processing elements to complex entities exhibiting rational behavior. The book is organized into three parts. Part I addresses ways to develop control abstractions that efficiently guide problem-solving; communication abstractions that yield cooperation; and description abstractions that result in effective organizational structure. Part II describes architectures for developing and testing DAI systems. Part III discusses applications of DAI in manufacturing, office automation, and man-machine interactions. This book is intended for researchers, system developers, and students in artificial intelligence and related disciplines. It can also be used as a reference for students and researchers in other disciplines, such as psychology, philosophy, robotics, and distributed computing, who wish to understand the issues of DAI.
