Representing and Reasoning with Probabilistic Knowledge

A Logical Approach to Probabilities

Author: Fahiem Bacchus

Publisher: Cambridge, Mass. : MIT Press

ISBN: N.A

Category: Computers

Page: 233

View: 6373

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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 first­order 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. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.

Uncertainty in Artificial Intelligence

Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C. 1993

Author: David Heckerman,Abe Mamdani

Publisher: Morgan Kaufmann

ISBN: 1483214516

Category: Computers

Page: 552

View: 6304

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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.

Probabilistic Reasoning in Intelligent Systems

Networks of Plausible Inference

Author: Judea Pearl

Publisher: Morgan Kaufmann

ISBN: 9781558604797

Category: Computers

Page: 552

View: 6049

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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.

Uncertainty in Artificial Intelligence

Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994

Author: MKP

Publisher: Elsevier

ISBN: 1483298604

Category: Computers

Page: 614

View: 343

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Uncertainty Proceedings 1994

Logics in Artificial Intelligence

9th European Conference, JELIA 2004, Lisbon, Portugal, September 27-30, 2004, Proceedings

Author: Jose, Julio Alferes

Publisher: Springer Science & Business Media

ISBN: 3540232427

Category: Computers

Page: 744

View: 9575

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This book constitutes the refereed proceedings of the 9th European Conference on Logics in Artificial Intelligence, JELIA 2004, held in Lisbon, Portugal, in September 2004. The 52 revised full papers and 15 revised systems presentation papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from a total of 169 submissions. The papers are organized in topical sections on multi-agent systems; logic programming and nonmonotonic reasoning; reasoning under uncertainty; logic programming; actions and causation; complexity; description logics; belief revision; modal, spatial, and temporal logics; theorem proving; and applications.

Reasoning about Uncertainty

Author: Joseph Y. Halpern

Publisher: MIT Press

ISBN: 026234050X

Category: Computers

Page: 504

View: 1609

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In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks.This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

Representing Uncertain Knowledge

An Artificial Intelligence Approach

Author: Paul Krause,Dominic Clark

Publisher: Springer Science & Business Media

ISBN: 9401120846

Category: Computers

Page: 277

View: 9147

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The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for malisms. In this book, we offer a broad perspective on uncertainty and approach es to managing uncertainty. Rather than provide a daunting mass of techni cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current research di rections in the field. The intended readership will include researchers and practitioners in volved in the design and implementation of Decision Support Systems, Ex pert Systems, other Knowledge-Based Systems and in Cognitive Science.

Uncertainty in Artificial Intelligence

Proceedings of the ... Conference on Uncertainty in Artificial Intelligence

Author: Eric Joel Horvitz,Dan Geiger,Prakash P. Shenoy

Publisher: Morgan Kaufmann

ISBN: N.A

Category: Artificial intelligence

Page: N.A

View: 3377

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KI 2011: Advances in Artificial Intelligence

34th Annual German Conference on AI, Berlin, Germany, October 4-7,2011, Proceedings

Author: Joscha Bach,Stefan Edelkamp

Publisher: Springer

ISBN: 3642244556

Category: Computers

Page: 370

View: 6066

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This book constitutes the refereed proceedings of the 34th Annual German Conference on Artificial Intelligence, KI 2011, held in Berlin, Germany, in October 2011. The 32 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 81 submissions. The papers are divided in topical sections on computational learning and datamining, knowledge representation and reasonings, augmented reality, swarm intelligence; and planning and scheduling.

Bayesian Rationality

The Probabilistic Approach to Human Reasoning

Author: Mike Oaksford,Nick Chater

Publisher: Oxford University Press

ISBN: 9780198524496

Category: Philosophy

Page: 330

View: 7446

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For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.

An Inductive Logic Programming Approach to Statistical Relational Learning

Author: Kristian Kersting

Publisher: IOS Press

ISBN: 9781586036744

Category: Computers

Page: 228

View: 1877

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Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Information and Knowledge Management

CIKM .... Proceedings of the ISMM International Conference

Author: Forouzan Golshani,Kia Makki,Association for Computing Machinery. Special Interest Group on Information Retrieval

Publisher: N.A

ISBN: 9780897919708

Category: Database management

Page: 378

View: 3366

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Artificial Intelligence

Author: David L. Poole,Alan K. Mackworth

Publisher: Cambridge University Press

ISBN: 110719539X

Category: Computers

Page: 760

View: 9269

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Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Foundations of Decision-Making Agents

Logic, Probability and Modality

Author: Subrata Kumar Das

Publisher: World Scientific

ISBN: 9812779841

Category: Business & Economics

Page: 366

View: 4705

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This self-contained book provides three fundamental and generic approaches (logical, probabilistic, and modal) to representing and reasoning with agent epistemic states, specifically in the context of decision making. Each of these approaches can be applied to the construction of intelligent software agents for making decisions, thereby creating computational foundations for decision-making agents. In addition, the book introduces a formal integration of the three approaches into a single unified approach that combines the advantages of all the approaches. Finally, the symbolic argumentation approach to decision making developed in this book, combining logic and probability, offers several advantages over the traditional approach to decision making which is based on simple rule-based expert systems or expected utility theory. Sample Chapter(s). Chapter 1: Modeling Agent Epistemic States: An Informal Overview (202 KB). Contents: Modeling Agent Epistemic States: An Informal Overview; Mathematical Preliminaries; Classical Logics for the Propositional Epistemic Model; Logic Programming; Logical Rules for Making Decisions; Bayesian Belief Networks; Influence Diagrams for Making Decisions; Modal Logics for the Possible World Epistemic Model; Symbolic Argumentation for Decision Making. Readership: Undergraduates and graduates majoring in artificial intelligence, computer professionals and researchers from the decision science community.

STACS 97

14th Annual Symposium on Theoretical Aspects of Computer Science, Lübeck, Germany, February 27 - March 1, 1997 Proceedings

Author: Rüdiger Reischuk,Michel Morvan

Publisher: Springer Science & Business Media

ISBN: 9783540626169

Category: Computers

Page: 614

View: 7330

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This book constitutes the refereed proceedings of the 14th Annual Symposium on Theoretical Aspects of Computer Science, STACS 97, held in Lübeck, Germany, in February/March 1997. The 46 revised full papers included were carefully selected from a total of 139 submissions; also included are three invited full papers. The papers presented span the whole scope of theoretical computer science. Among the topics covered are, in particular, algorithms and data structures, computational complexity, automata and formal languages, structural complexity, parallel and distributed systems, parallel algorithms, semantics, specification and verification, logic, computational geometry, cryptography, learning and inductive inference.

Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

ISBN: 0521518148

Category: Computers

Page: 697

View: 9517

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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Logical and Relational Learning

Author: Luc De Raedt

Publisher: Springer Science & Business Media

ISBN: 3540688560

Category: Computers

Page: 387

View: 3671

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This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.