Download or read book Neural Symbolic Cognitive Reasoning written by Artur S. D'Avila Garcez and published by Springer Science & Business Media. This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Download or read book Neural Symbolic Learning Systems written by Artur S. d'Avila Garcez and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Download or read book Neuro Symbolic Artificial Intelligence The State of the Art written by P. Hitzler and published by IOS Press. This book was released on 2022-01-19 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.
Download or read book Computational Architectures Integrating Neural and Symbolic Processes written by Ron Sun and published by Springer Science & Business Media. This book was released on 1994-11-30 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches. Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.
Download or read book Human Reasoning and Cognitive Science written by Keith Stenning and published by MIT Press. This book was released on 2012-01-13 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new proposal for integrating the employment of formal and empirical methods in the study of human reasoning. In Human Reasoning and Cognitive Science, Keith Stenning and Michiel van Lambalgen—a cognitive scientist and a logician—argue for the indispensability of modern mathematical logic to the study of human reasoning. Logic and cognition were once closely connected, they write, but were “divorced” in the past century; the psychology of deduction went from being central to the cognitive revolution to being the subject of widespread skepticism about whether human reasoning really happens outside the academy. Stenning and van Lambalgen argue that logic and reasoning have been separated because of a series of unwarranted assumptions about logic. Stenning and van Lambalgen contend that psychology cannot ignore processes of interpretation in which people, wittingly or unwittingly, frame problems for subsequent reasoning. The authors employ a neurally implementable defeasible logic for modeling part of this framing process, and show how it can be used to guide the design of experiments and interpret results.
Download or read book Cognitive Modeling written by Thad A. Polk and published by MIT Press. This book was released on 2002 with total page 1300 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to the computational modeling of human cognition.
Download or read book Uncertainty Management with Fuzzy and Rough Sets written by Rafael Bello and published by Springer. This book was released on 2019-01-22 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a timely overview of fuzzy and rough set theories and methods. Based on selected contributions presented at the International Symposium on Fuzzy and Rough Sets, ISFUROS 2017, held in Varadero, Cuba, on October 24-26, 2017, the book also covers related approaches, such as hybrid rough-fuzzy sets and hybrid fuzzy-rough sets and granular computing, as well as a number of applications, from big data analytics, to business intelligence, security, robotics, logistics, wireless sensor networks and many more. It is intended as a source of inspiration for PhD students and researchers in the field, fostering not only new ideas but also collaboration between young researchers and institutions and established ones.
Download or read book Hybrid Neural Systems written by Stefan Wermter and published by Springer Science & Business Media. This book was released on 2000-03-29 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems. The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.
Download or read book The Cambridge Handbook of Thinking and Reasoning written by Keith J. Holyoak and published by Cambridge University Press. This book was released on 2005-04-18 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cambridge Handbook of Thinking and Reasoning is the first comprehensive and authoritative handbook covering all the core topics of the field of thinking and reasoning. Written by the foremost experts from cognitive psychology, cognitive science, and cognitive neuroscience, individual chapters summarize basic concepts and findings for a major topic, sketch its history, and give a sense of the directions in which research is currently heading. The volume also includes work related to developmental, social and clinical psychology, philosophy, economics, artificial intelligence, linguistics, education, law, and medicine. Scholars and students in all these fields and others will find this to be a valuable collection.
Download or read book Artificial Intelligence XXXVII written by Max Bramer and published by Springer Nature. This book was released on 2020-12-08 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020, which was supposed to be held in Cambridge, UK, in December 2020. The conference was held virtually due to the COVID-19 pandemic. The 23 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 44 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: neural nets and knowledge management; machine learning; industrial applications; advances in applied AI; and medical and legal applications.
Download or read book Neural Networks for Knowledge Representation and Inference written by Daniel S. Levine and published by Psychology Press. This book was released on 2013-04-15 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.
Download or read book Reasoning written by Daniel Krawczyk and published by Academic Press. This book was released on 2017-11-13 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reasoning: The Neuroscience of How We Think is a comprehensive guide to the core topics related to a thorough understanding of reasoning. It presents the current knowledge of the subject in a unified, complete manner, ranging from animal studies, to applied situations, and is the only book available that presents a sustained focus on the neurobiological processes behind reasoning throughout all chapters, while also synthesizing research from animal behavior, cognitive psychology, development, and philosophy for a truly multidisciplinary approach. The book considers historical perspectives, state-of-the-art research methods, and future directions in emerging technology and cognitive enhancement. Written by an expert in the field, this book provides a coherent and structured narrative appropriate for students in need of an introduction to the topic of reasoning as well as researchers seeking well-rounded foundational content. It is essential reading for neuroscientists, cognitive scientists, neuropsychologists and others interested in the neural mechanisms behind thinking, reasoning and higher cognition. Provides a comparative perspective considering animal cognition and its relevance to human reasoning Includes developmental and lifespan considerations throughout the book Discusses technological development and its role in reasoning, both currently and in the future Considers perspectives from not only neuroscience, but cognitive psychology, philosophy, development, and animal behavior for a multidisciplinary treatment Contains highlight boxes featuring additional details on methods, historical descriptions and experimental tasks
Download or read book The Myth of Artificial Intelligence written by Erik J. Larson and published by Harvard University Press. This book was released on 2021-04-06 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own.
Download or read book The Oxford Handbook of Causal Reasoning written by Michael Waldmann and published by Oxford University Press. This book was released on 2017 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields.
Download or read book The Symbolic and Connectionist Paradigms written by John Dinsmore and published by Psychology Press. This book was released on 2014-01-14 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern study of cognition finds itself with two widely endorsed but seemingly incongruous theoretical paradigms. The first of these, inspired by formal logic and the digital computer, sees reasoning in the principled manipulation of structured symbolic representations. The second, inspired by the physiology of the brain, sees reasoning as the behavior that emerges from the direct interactions found in large networks of simple processing components. Each paradigm has its own accomplishments, problems, methodology, proponents, and agenda. This book records the thoughts of researchers -- from both computer science and philosophy -- on resolving the debate between the symbolic and connectionist paradigms. It addresses theoretical and methodological issues throughout, but at the same time exhibits the current attempts of practicing cognitive scientists to solve real problems.
Download or read book Cognitive Design for Artificial Minds written by Antonio Lieto and published by Routledge. This book was released on 2021-03-31 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration. Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.
Download or read book Neuro Symbolic Reasoning and Learning written by Paulo Shakarian and published by Springer. This book was released on 2023-09-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the key results and frameworks for various NSAO tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.
