Uncertainty In Computational Intelligence Based Decision Making
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Uncertainty In Computational Intelligence Based Decision Making
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Author : Ali Ahmadian
language : en
Publisher: Elsevier
Release Date : 2024-09-23
Uncertainty In Computational Intelligence Based Decision Making written by Ali Ahmadian and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-23 with Computers categories.
Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision
Computational Intelligence For Business Analytics
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2021-10-26
Computational Intelligence For Business Analytics written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-26 with Technology & Engineering categories.
Corporate success has been changed by the importance of new developments in Business Analytics (BA) and furthermore by the support of computational intelligence- based techniques. This book opens a new avenues in these subjects, identifies key developments and opportunities. The book will be of interest for students, researchers and professionals to identify innovative ways delivered by Business Analytics based on computational intelligence solutions. They help elicit information, handle knowledge and support decision-making for more informed and reliable decisions even under high uncertainty environments.Computational Intelligence for Business Analytics has collected the latest technological innovations in the field of BA to improve business models related to Group Decision-Making, Forecasting, Risk Management, Knowledge Discovery, Data Breach Detection, Social Well-Being, among other key topics related to this field.
Uncertainty Modeling In Knowledge Engineering And Decision Making
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Author :
language : en
Publisher: World Scientific
Release Date : 2012
Uncertainty Modeling In Knowledge Engineering And Decision Making written by and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view. Sample Chapter(s). Foreword (55 KB). Evaluation of Manufacturing Technology of Photovoltaic Cells (124 KB). Contents: Decision Making and Decision Support Systems; Uncertainty Modeling; Foundations of Computational Intelligence; Statistics, Data Analysis and Data Mining; Intelligent Information Processing; Productivity and Reliability; Applied Research. Readership: Graduate students, researchers, and academics in artificial intelligence/machine learning, information management, decision sciences, databases/information sciences and fuzzy logic.
Intelligent Decision Making An Ai Based Approach
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Author : Gloria Phillips-Wren
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-04
Intelligent Decision Making An Ai Based Approach written by Gloria Phillips-Wren 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 2008-03-04 with Mathematics categories.
Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.
Decision Making Uncertainty Imperfection Deliberation And Scalability
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Author : Tatiana V. Guy
language : en
Publisher: Springer
Release Date : 2015-02-09
Decision Making Uncertainty Imperfection Deliberation And Scalability written by Tatiana V. Guy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-09 with Technology & Engineering categories.
This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: • task allocation to maximize “the wisdom of the crowd”; • design of a society of “edutainment” robots who account for one anothers’ emotional states; • recognizing and counteracting seemingly non-rational human decision making; • coping with extreme scale when learning causality in networks; • efficiently incorporating expert knowledge in personalized medicine; • the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.
Applications Of Artificial Intelligence For Decision Making
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Author : Patrick J Talbot
language : en
Publisher: CreateSpace
Release Date : 2015-04-10
Applications Of Artificial Intelligence For Decision Making written by Patrick J Talbot and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-10 with categories.
Book Description: We have the data! Too much data! Decision-making requires that data be filtered and refined to provide information. Adding context to the content produces actionable knowledge. Unfortunately, current techniques strip away the uncertainty associated with the raw data. This book provides a decision-centered approach for coping with uncertainty that combines what people do best with what computers do best. Algorithms "plug into"the knowledge base from a single import/export interface, facilitating multi-strategy reasoning. Triage filters the data, extraction of hedge words capture uncertainty, an executable knowledge base provides content in context, data fusion propagates uncertainty, data analytics discover patterns, and plan optimization tools move the decision-maker from "what's going on" to "what to do." Displays present actionable knowledge with associated uncertainties explicitly shown. Fifteen applications are shown ranging from longevity prediction, to a retail problem solver, to intelligence community applications, to starship cybernetics. We wrote the book to provide the practitioner with compelling ideas for orchestrating artificial intelligence, statistical, and mathematical algorithms to produce fully integrated decision support systems. Novel techniques of particular interest are: a knowledge representation that provides a unifying framework for multi-strategy reasoning and simulation, a robust treatment of uncertainty, monitor-assess-plan-execute decision loops for routine and quick-reaction decisions, eight techniques for automated discovery of unknown unknowns, level 4 (process refinement) data fusion, and a self-aware knowledge base that "knows what it knows."
Decision Making Under Uncertainty
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2015-07-17
Decision Making Under Uncertainty written by Mykel J. Kochenderfer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-17 with Computers categories.
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.
Computational Intelligence In Complex Decision Making Systems
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Author : Ruan Da
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-01
Computational Intelligence In Complex Decision Making Systems written by Ruan Da 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 2010-06-01 with Computers categories.
In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.
Decision Theory Models For Applications In Artificial Intelligence Concepts And Solutions
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Author : Sucar, L. Enrique
language : en
Publisher: IGI Global
Release Date : 2011-10-31
Decision Theory Models For Applications In Artificial Intelligence Concepts And Solutions written by Sucar, L. Enrique and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-31 with Computers categories.
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.
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.