Representing And Reasoning With Probabilistic Knowledge
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Representing And Reasoning With Probabilistic Knowledge
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Author : Fahiem Bacchus
language : en
Publisher: Cambridge, Mass. : MIT Press
Release Date : 1990
Representing And Reasoning With Probabilistic Knowledge written by Fahiem Bacchus and has been published by Cambridge, Mass. : MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.
Representing And Reasoning With Probabilistic Knowledge
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Author : Fahiem Bacchus
language : en
Publisher: Faculty of Mathematics, University of Waterloo
Release Date : 1988
Representing And Reasoning With Probabilistic Knowledge written by Fahiem Bacchus and has been published by Faculty of Mathematics, University of Waterloo this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Artificial intelligence categories.
Quantified Representation Of Uncertainty And Imprecision
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Author : Dov M. Gabbay
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Quantified Representation Of Uncertainty And Imprecision written by Dov M. Gabbay and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Philosophy categories.
We are happy to present the first volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems. Uncertainty pervades the real world and must therefore be addressed by every system that attempts to represent reality. The representation of uncertainty is a ma jor concern of philosophers, logicians, artificial intelligence researchers and com puter sciencists, psychologists, statisticians, economists and engineers. The present Handbook volumes provide frontline coverage of this area. This Handbook was produced in the style of previous handbook series like the Handbook of Philosoph ical Logic, the Handbook of Logic in Computer Science, the Handbook of Logic in Artificial Intelligence and Logic Programming, and can be seen as a companion to them in covering the wide applications of logic and reasoning. We hope it will answer the needs for adequate representations of uncertainty. This Handbook series grew out of the ESPRIT Basic Research Project DRUMS II, where the acronym is made out of the Handbook series title. This project was financially supported by the European Union and regroups 20 major European research teams working in the general domain of uncertainty. As a fringe benefit of the DRUMS project, the research community was able to create this Hand book series, relying on the DRUMS participants as the core of the authors for the Handbook together with external international experts.
Probabilistic Reasoning In Intelligent Systems
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Author : Judea Pearl
language : en
Publisher: Morgan Kaufmann
Release Date : 1988-09
Probabilistic Reasoning In Intelligent Systems written by Judea Pearl and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-09 with Computers categories.
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Knowledge Representation And Reasoning
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Author : Ronald Brachman
language : en
Publisher: Morgan Kaufmann
Release Date : 2004-05-19
Knowledge Representation And Reasoning written by Ronald Brachman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-19 with Computers categories.
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.
Uncertainty In Artificial Intelligence
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Author : David Heckerman
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-12
Uncertainty In Artificial Intelligence written by David Heckerman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Computers categories.
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.
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Author :
language : en
Publisher: IOS Press
Release Date :
written by and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Cognitive Foundations Of Agentic Ai From Theory To Practice
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Cognitive Foundations Of Agentic Ai From Theory To Practice written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Cognitive Foundations of Agentic AI: From Theory to Practice explores the conceptual and technical underpinnings of AI systems that act with autonomy, proactivity, and social intelligence. Drawing from cognitive science, artificial intelligence, and systems theory, this book provides a structured view of how intelligent agents perceive, learn, reason, and interact in dynamic environments. Beginning with a detailed exploration of what defines Agentic AI, the book delves into the cognitive processes that support agency—perception, learning, reasoning, memory, and decision-making. It bridges classical symbolic models with modern deep learning and neuro-symbolic systems to illustrate how hybrid architectures can enable generalizable, goal-driven behavior. Emphasis is placed on modeling real-world complexity, social cognition, and human-like interaction through language, emotional awareness, and theory of mind. The text also critically examines challenges such as generalization, ethical alignment, uncertainty, and explainability. Through illustrative case studies in robotics, healthcare, digital assistants, and multi-agent systems, it highlights the real-world implications and limitations of agentic systems. The final chapters outline practical pathways to building cognitive agents, including architecture design, training environments, and evaluation methods. It encourages a collaborative AI-human future where agents not only support but enhance human decision-making, learning, and creativity. Ideal for AI practitioners, researchers, and graduate students, the book offers both a theoretical framework and practical insights into creating autonomous systems that think, learn, and act intelligently. It invites readers to rethink intelligence not as a fixed trait but as an emergent, contextual process deeply rooted in cognition.
Probabilistic Thinking
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Author : Egan J. Chernoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-05
Probabilistic Thinking written by Egan J. Chernoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-05 with Education categories.
This volume provides a necessary, current and extensive analysis of probabilistic thinking from a number of mathematicians, mathematics educators, and psychologists. The work of 58 contributing authors, investigating probabilistic thinking across the globe, is encapsulated in 6 prefaces, 29 chapters and 6 commentaries. Ultimately, the four main perspectives presented in this volume (Mathematics and Philosophy, Psychology, Stochastics and Mathematics Education) are designed to represent probabilistic thinking in a greater context.
The Semantic Web
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Author : Jeff Z. Pan
language : en
Publisher: Springer
Release Date : 2012-06-02
The Semantic Web written by Jeff Z. Pan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-02 with Computers categories.
This book constitutes the refereed post-proceedings of the Joint International Semantic Technology Conference, JIST 2011, held in Hangzhou, China, in December 2011. This conference is a joint event for regional semantic Web related conferences. JIST 2011 brings together the Asian Semantic Web Conference 2011 and the Chinese Semantic Web Conference 2011. The 21 revised full papers presented together with 12 short papers were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics in disciplines related to semantic technology including applications of the semantic Web, management of semantic Web data, ontology and reasoning, social semantic Web, and user interfaces to the semantic Web.