Advance Chess Inferential Analysis Of Distributive Cognitive Logistics Book 2 Vol 1
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Advance Chess Inferential Analysis Of Distributive Cognitive Logistics Book 2 Vol 1
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Author : Siafa B. Neal
language : en
Publisher:
Release Date : 2019-09-26
Advance Chess Inferential Analysis Of Distributive Cognitive Logistics Book 2 Vol 1 written by Siafa B. Neal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-26 with Games & Activities categories.
Book 2 Vol. 1 is about the Double Set game played on an advance chess game board named Model III, the Longitudinal Star Gate 14 Model. A Double set game involves the use of two chess sets whereby the winner must checkmate his/her opponent's kings twice.
Advance Chess Inferential View Analysis Of The Double Set Game D 2 30 Robotic Intelligence Possibilities The Double Set Game Book 2
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Author : Siafa B. Neal
language : en
Publisher:
Release Date : 2019-10-11
Advance Chess Inferential View Analysis Of The Double Set Game D 2 30 Robotic Intelligence Possibilities The Double Set Game Book 2 written by Siafa B. Neal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-11 with Games & Activities categories.
This book is about the Double Set Game (D.2.30) played on the game board, Model III, S.G. 14 Model. A double Set game involves two chess set games played on a single game board.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
The Semantic Sphere 1
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Author : Pierre Lévy
language : en
Publisher: John Wiley & Sons
Release Date : 2013-01-22
The Semantic Sphere 1 written by Pierre Lévy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-22 with Technology & Engineering categories.
The new digital media offers us an unprecedented memory capacity, an ubiquitous communication channel and a growing computing power. How can we exploit this medium to augment our personal and social cognitive processes at the service of human development? Combining a deep knowledge of humanities and social sciences as well as a real familiarity with computer science issues, this book explains the collaborative construction of a global hypercortex coordinated by a computable metalanguage. By recognizing fully the symbolic and social nature of human cognition, we could transform our current opaque global brain into a reflexive collective intelligence.
Autonomous Horizons
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Author : Greg Zacharias
language : en
Publisher: Independently Published
Release Date : 2019-04-05
Autonomous Horizons written by Greg Zacharias and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-05 with categories.
Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Human Factors Methods
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Author : Neville Stanton
language : en
Publisher: Ashgate Publishing, Ltd.
Release Date : 2013
Human Factors Methods written by Neville Stanton and has been published by Ashgate Publishing, Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
This second edition of Human Factors Methods: A Practical Guide for Engineering and Design now presents 107 design and evaluation methods including numerous refinements to those that featured in the original. The book acts as an ergonomics methods manual, aiding both students and practitioners. Offering a 'how-to' text on a substantial range of ergonomics methods, the eleven sections represent the different categories of ergonomics methods and techniques that can be used in the evaluation and design process.
Cognitive Biases In Visualizations
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Author : Geoffrey Ellis
language : en
Publisher: Springer
Release Date : 2018-09-27
Cognitive Biases In Visualizations written by Geoffrey Ellis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-27 with Computers categories.
This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users’ decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly.
The Soar Cognitive Architecture
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Author : John E. Laird
language : en
Publisher: MIT Press
Release Date : 2019-08-20
The Soar Cognitive Architecture written by John E. Laird and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-20 with Computers categories.
The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
Decision Making Under Uncertainty
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2015-07-24
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 2015-07-24 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.