Reasoning about Uncertainty, second edition

Author: Joseph Y. Halpern
Publisher: MIT Press
ISBN: 0262533804
Category : Computers
Languages : en
Pages : 505

Book Description
Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. 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.

Readings in Uncertain Reasoning

Author: Glenn Shafer
Publisher: Morgan Kaufmann Publishers
ISBN:
Category : Computers
Languages : en
Pages : 788

Book Description
Computing Methodologies -- Artificial Intelligence.

Subjective Logic

Author: Audun Jøsang
Publisher: Springer
ISBN: 3319423371
Category : Computers
Languages : en
Pages : 355

Book Description
This is the first comprehensive treatment of subjective logic and all its operations. The author developed the approach, and in this book he first explains subjective opinions, opinion representation, and decision-making under vagueness and uncertainty, and he then offers a full definition of subjective logic, harmonising the key notations and formalisms, concluding with chapters on trust networks and subjective Bayesian networks, which when combined form general subjective networks. The author shows how real-world situations can be realistically modelled with regard to how situations are perceived, with conclusions that more correctly reflect the ignorance and uncertainties that result from partially uncertain input arguments. The book will help researchers and practitioners to advance, improve and apply subjective logic to build powerful artificial reasoning models and tools for solving real-world problems. A good grounding in discrete mathematics is a prerequisite.

Inferential Models

Author: Ryan Martin
Publisher: CRC Press
ISBN: 1439886512
Category : Mathematics
Languages : en
Pages : 274

Book Description
A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning

Beyond Uncertainty

Author: Katie Steele
Publisher: Cambridge University Press
ISBN: 1108608043
Category : Science
Languages : en
Pages : 120

Book Description
The main aim of this Element is to introduce the topic of limited awareness, and changes in awareness, to those interested in the philosophy of decision-making and uncertain reasoning. While it has long been of interest to economists and computer scientists, this topic has only recently been subject to philosophical investigation. Indeed, at first sight limited awareness seems to evade any systematic treatment: it is beyond the uncertainty that can be managed. On the one hand, an agent has no control over what contingencies she is and is not aware of at a given time, and any awareness growth takes her by surprise. On the other hand, agents apparently learn to identify the situations in which they are more and less likely to experience limited awareness and subsequent awareness growth. How can these two sides be reconciled? That is the puzzle we confront in this Element.

Probabilistic Reasoning in Intelligent Systems

Author: Judea Pearl
Publisher: Elsevier
ISBN: 0080514898
Category : Computers
Languages : en
Pages : 573

Book Description
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.

Clinical Reasoning: Knowledge, Uncertainty, and Values in Health Care

Author: Daniele Chiffi
Publisher: Springer Nature
ISBN: 3030590941
Category : Diagnosis
Languages : en
Pages : 168

Book Description
This book offers a philosophically-based, yet clinically-oriented perspective on current medical reasoning aiming at 1) identifying important forms of uncertainty permeating current clinical reasoning and practice 2) promoting the application of an abductive methodology in the health context in order to deal with those clinical uncertainties 3) bridging the gap between biomedical knowledge, clinical practice, and research and values in both clinical and philosophical literature. With a clear philosophical emphasis, the book investigates themes lying at the border between several disciplines, such as medicine, nursing, logic, epistemology, and philosophy of science; but also ethics, epidemiology, and statistics. At the same time, it critically discusses and compares several professional approaches to clinical practice such as the one of medical doctors, nurses and other clinical practitioners, showing the need for developing a unified framework of reasoning, which merges methods and resources from many different clinical but also non-clinical disciplines. In particular, this book shows how to leverage nursing knowledge and practice, which has been considerably neglected so far, to further shape the interdisciplinary nature of clinical reasoning. Furthermore, a thorough philosophical investigation on the values involved in health care is provided, based on both the clinical and philosophical literature. The book concludes by proposing an integrative approach to health and disease going beyond the so-called "classical biomedical model of care".

Reasoning About Knowledge

Author: Ronald Fagin
Publisher: MIT Press
ISBN: 9780262562003
Category : Business & Economics
Languages : en
Pages : 576

Book Description
Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms. Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes.

Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262331713
Category : Computers
Languages : en
Pages : 350

Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Probability Theory

Author:
Publisher: Allied Publishers
ISBN: 9788177644517
Category :
Languages : en
Pages : 436

Book Description
Probability theory