Download or read book Hierarchical Learning and Planning in Partially Observable Markov Decision Processes written by Georgios Theocharous and published by . This book was released on 2002 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Planning with Markov Decision Processes written by Mausam Natarajan and published by Springer Nature. This book was released on 2022-06-01 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Download or read book Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

Download or read book Handbook of Learning and Approximate Dynamic Programming written by Jennie Si and published by John Wiley & Sons. This book was released on 2004-08-02 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors are leading researchers in the field

Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.

Download or read book Abstraction Reformulation and Approximation written by Sven Koenig and published by Springer. This book was released on 2003-08-02 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been recognized since the inception of Artificial Intelligence (AI) that abstractions, problem reformulations, and approximations (AR&A) are central to human common sense reasoning and problem solving and to the ability of systems to reason effectively in complex domains. AR&A techniques have been used to solve a variety of tasks, including automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving. The primary purpose of AR&A techniques in such settings is to overcome computational intractability. In addition, AR&A techniques are useful for accelerating learning and for summarizing sets of solutions. This volume contains the proceedings of SARA 2002, the fifth Symposium on Abstraction, Reformulation, and Approximation, held at Kananaskis Mountain Lodge, Kananaskis Village, Alberta (Canada), August 2 4, 2002. The SARA series is the continuation of two separate threads of workshops: AAAI workshops in 1990 and 1992, and an ad hoc series beginning with the "Knowledge Compilation" workshop in 1986 and the "Change of Representation and Inductive Bias" workshop in 1988 with followup workshops in 1990 and 1992. The two workshop series merged in 1994 to form the first SARA. Subsequent SARAs were held in 1995, 1998, and 2000.

Download or read book Planning with Markov Decision Processes written by Mausam Natarajan and published by Springer. This book was released on 2012-07-03 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Download or read book The Complexity of Planning with Partially observable Markov Decision Processes written by Martin Mundhenk and published by . This book was released on 2000 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hierarchical Control and Learning for Markov Decision Processes written by Ronald Edward Parr and published by . This book was released on 1998 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.

Download or read book Abstraction Reformulation and Approximation written by Jean-Daniel Zucker and published by Springer. This book was released on 2005-08-25 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the 6th Symposium on Abstraction, Reformulation and Approximation (SARA 2005). The symposium was held at Airth Castle, Scotland, UK, from July 26th to 29th, 2005, just prior to the IJCAI 2005 conference in Edinburgh.

Download or read book Advances in Neural Information Processing Systems 19 written by Bernhard Schölkopf and published by MIT Press. This book was released on 2007 with total page 1668 pages. Available in PDF, EPUB and Kindle. Book excerpt: The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.

Download or read book Theory and Applications of Models of Computation written by Jin-Yi Cai and published by Springer. This book was released on 2006-05-05 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Theory and Applications of Models of Computation, TAMC 2006, held in Beijing, China, in May 2006. The 75 revised full papers presented together with 7 plenary talks were carefully reviewed and selected from 319 submissions. All major areas in computer science, mathematics (especially logic) and the physical sciences particularly with regard to computation and computability theory are addressed.

Download or read book Information Theoretic Learning Methods for Markov Decision Processes with Parametric Uncertainty written by Peeyush Kumar and published by . This book was released on 2018 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov decision processes (MDPs) model a class of stochastic sequential decision problems with applications in engineering, medicine, and business analytics. There is considerable interest in the literature in MDPs with imperfect information, were the search for well-performing policies faces many challenges. There is no rigorous universally accepted optimality criterion. The search space explodes and the decision-maker suffers from the curse-of-dimensionality. Finding good policies requires careful balancing of the trade-off between exploration to acquire information and exploitation of this information to earn high rewards. This dissertation contributes to this area by building a rigorous framework rooted in information theory for solving MDPs with model uncertainty. In the first chapter, the value of a parameter that characterizes the transition probabilities is unkown to the decision-maker. The decision-maker updates its Bayesian belief about this parameter using state observations induced by policies it chooses. Information Directed Policy Sampling (IDPS) is proposed to manage the exploration-exploitation trade-off. At each time-stage, the decision-maker solves a convex problem to sample a policy from a distribution that minimizes a particular ratio. The numerator of this ratio equals the square of the expected regret of distributions over policy trajectories (exploitation). The denominator equals the expected mutual information between the resulting system-state trajectory and the parameter's posterior (exploration). A generalization of Hoeffding's inequality is employed to bound regret. The bound grows at a square-root rate with the planning horizon, and a square-root log-linear rate with the parameter-set cardinality. It is insensitive to state and action-space cardinalities. The regret per stage converges to zero as the planning horizon increases. IDPS is thus asymptotically optimal. Numerical results on a stylized example, an auction-design problem, and a response-guided dosing problem demonstrate its benefits. Uncertainty in transition probablities arises from two levels in the second chapter. The top level corresponds to the ambiguity about the system model. Bottom-level uncertainty is rooted in the unknown parameter values for each possible model. Prior-update formulas using a hierarchical Bayesian framework are derived and incorporated into two learning algorithms: Thompson Sampling and a hierarchical extension of IDPS. Analytical performance bounds for these algorithms are developed. Numerical results on the response-guided dosing problem, which is amenable to hierarchical modeling, are presented. The third chapter extends the above to partially observable Markov decision processes (POMDPs). In POMDPs, the decision-maker cannot observe the acutal state of the system. Instead, it can take a measurement that provides probablitistic information about the true state. Such POMDPs are equivalent to Bayesian adaptive MDPs (BAMDPs) fromt he first two chapters. This connection is exploited to devise alogrithms and provide analytical performance guarantees for POMDPs in three separate cases: a) uncertainty in the transition probablities; b) uncertainty in the measurement outcome probabilities; and c) uncertainty in both. Numerical results on partially observed response-guided dosing are included. the fourth chapter proposes a formal information theoretic framework inspired by stochastic thermodynamics. It utilizes the idea that information is physical. An explicit link between information entropy and stochastic dynamics of a system coupled to an environment is developed from fundamental principles. Unlike the heuristic method of defining information ratio, this provides an optimization program that is built from system dynamics, problem objective, and the feedback from observations. To the best of my knowledge, this is the first comprehensive work in MDPs with model uncertainty, which builds a problem formulation entirely grounded in system and information dynamics without the use of ad-hoc heuristics.

Download or read book The Logic of Adaptive Behavior written by Martijn van Otterlo and published by IOS Press. This book was released on 2009 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.

Download or read book Machine Learning and Data Mining for Computer Security written by Marcus A. Maloof and published by Springer Science & Business Media. This book was released on 2006-02-27 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.

Download or read book Planning with Partially Observable Markov Decision Processes written by Siu Shan Lee and published by . This book was released on 2000 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: