Markov Decision Processes With Their Applications

Author: Qiying Hu
Editor: Springer Science & Business Media
ISBN: 0387369511
Size: 11,18 MB
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Put together by two top researchers in the Far East, this text examines Markov Decision Processes - also called stochastic dynamic programming - and their applications in the optimal control of discrete event systems, optimal replacement, and optimal allocations in sequential online auctions. This dynamic new book offers fresh applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions.

Developments In Intelligent Agent Technologies And Multi Agent Systems Concepts And Applications

Author: Trajkovski, Goran
Editor: IGI Global
ISBN: 1609601734
Size: 13,68 MB
Format: PDF
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Developments in Intelligent Agent Technologies and Multi-Agent Systems: Concepts and Applications discusses research on emerging technologies and systems based on agent and multi-agent paradigms across various fields of science, engineering and technology. This book is a collection of work that covers conceptual frameworks, case studies, and analysis while serving as a medium of communication among researchers from academia, industry and government.

Advances On Practical Applications Of Agents And Multiagent Systems

Author: Yves Demazeau
Editor: Springer Science & Business Media
ISBN: 9783642198755
Size: 10,83 MB
Format: PDF, Docs
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PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is the international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2011 edition. These articles capture the most innovative results and this year’s trends: Finance and Trading, Information Systems and Organisations, Leisure Culture and Interactions, Medicine and Cloud Computing, Platforms and Adaptation, Robotics and Manufacturing, Security and Privacy, Transports and Optimisation paper.

Advances In Artificial Intelligence Iberamia 2014

Author: Ana L. C. Bazzan
Editor: Springer
ISBN: 3319120271
Size: 12,42 MB
Format: PDF, ePub
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This book constitutes the refereed proceedings of the 14th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2014, held in Santiago de Chile, Chile, in November 2014. The 64 papers presented were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections: knowledge engineering, knowledge representation and probabilistic reasoning; planning and scheduling; natural language processing; machine learning; fuzzy systems; knowledge discovery and data mining; bio-inspired computing; robotics; vision; multi-agent systems; agent-based modeling and simulation; AI in education, affective computing, and human-computer interaction; applications of AI; and ambient intelligence.

Advances In Information Retrieval

Author: Allan Hanbury
Editor: Springer
ISBN: 331916354X
Size: 17,69 MB
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This book constitutes the proceedings of the 37th European Conference on IR Research, ECIR 2015, held in Vienna, Austria, in March/April 2015. The 44 full papers, 41 poster papers and 7 demonstrations presented together with 3 keynotes in this volume were carefully reviewed and selected from 305 submissions. The focus of the papers were on following topics: aggregated search and diversity, classification, cross-lingual and discourse, efficiency, evaluation, event mining and summarisation, information extraction, recommender systems, semantic and graph-based models, sentiment and opinion, social media, specific search tasks, temporal models and features, topic and document models, user behavior and reproducible IR.

Markov Decision Processes In Artificial Intelligence

Author: Olivier Sigaud
Editor: John Wiley & Sons
ISBN: 1118620100
Size: 15,29 MB
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Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.

Simulation Based Algorithms For Markov Decision Processes

Author: Hyeong Soo Chang
Editor: Springer Science & Business Media
ISBN: 1447150228
Size: 12,17 MB
Format: PDF, Mobi
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Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.

Reinforcement Learning

Author: Richard S. Sutton
Editor: A Bradford Book
ISBN: 0262039249
Size: 16,59 MB
Format: PDF, Kindle
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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.

Meshfree Methods For Partial Differential Equations Iii

Author: Michael Griebel
Editor: Springer Science & Business Media
ISBN: 3540462228
Size: 10,48 MB
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Meshfree methods for the numerical solution of partial differential equations are becoming more and more mainstream in many areas of applications. This volume represents the state-of-the-art in meshfree methods. It consists of articles which address the different meshfree techniques, their mathematical properties and their application in applied mathematics, physics and engineering.