Planning with Markov Decision Processes

Planning with Markov Decision Processes
Author: Mausam,Andrey Kolobov
Publsiher: Morgan & Claypool Publishers
Total Pages: 194
Release: 2012
ISBN: 1608458865
Category: Computers
Language: EN, FR, DE, ES & NL

Planning with Markov Decision Processes 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

Markov Decision Processes in Artificial Intelligence

Markov Decision Processes in Artificial Intelligence
Author: Olivier Sigaud,Olivier Buffet
Publsiher: John Wiley & Sons
Total Pages: 480
Release: 2013-03-04
ISBN: 1118620100
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Markov Decision Processes in Artificial Intelligence 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). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.

Artificial Intelligence XXXV

Artificial Intelligence XXXV
Author: Max Bramer,Miltos Petridis
Publsiher: Springer
Total Pages: 454
Release: 2018-11-27
ISBN: 3030041913
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence XXXV Book Excerpt:

This book constitutes the proceedings of the 38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018, held in Cambridge, UK, in December 2018. The 25 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 46 submissions. There are technical and application papers which were organized in topical sections named: Neural Networks; Planning and Scheduling; Machine Learning; Industrial Applications of Artificial Intelligence; Planning and Scheduling in Action; Machine Learning in Action; Applications of Machine Learning; and Applications of Agent Systems and Genetic Algorithms.

Planning with Markov Decision Processes

Planning with Markov Decision Processes
Author: Mausam Natarajan,Andrey Poole
Publsiher: Springer Nature
Total Pages: 194
Release: 2022-06-01
ISBN: 3031015592
Category: Computers
Language: EN, FR, DE, ES & NL

Planning with Markov Decision Processes 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

Recent Advances in Reinforcement Learning

Recent Advances in Reinforcement Learning
Author: Leslie Pack Kaelbling
Publsiher: Springer
Total Pages: 292
Release: 2007-08-28
ISBN: 0585336563
Category: Computers
Language: EN, FR, DE, ES & NL

Recent Advances in Reinforcement Learning Book Excerpt:

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

Multi Objective Decision Making

Multi Objective Decision Making
Author: Diederik M. Roijers,Shimon Whiteson
Publsiher: Morgan & Claypool Publishers
Total Pages: 129
Release: 2017-04-20
ISBN: 1627056998
Category: Computers
Language: EN, FR, DE, ES & NL

Multi Objective Decision Making Book Excerpt:

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.

A Guided Tour of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research
Author: Pierre Marquis,Odile Papini,Henri Prade
Publsiher: Springer Nature
Total Pages: 523
Release: 2020-05-08
ISBN: 3030061671
Category: Computers
Language: EN, FR, DE, ES & NL

A Guided Tour of Artificial Intelligence Research Book Excerpt:

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This second volume presents the main families of algorithms developed or used in AI to learn, to infer, to decide. Generic approaches to problem solving are presented: ordered heuristic search, as well as metaheuristics are considered. Algorithms for processing logic-based representations of various types (first-order formulae, propositional formulae, logic programs, etc.) and graphical models of various types (standard constraint networks, valued ones, Bayes nets, Markov random fields, etc.) are presented. The volume also focuses on algorithms which have been developed to simulate specific ‘intelligent” processes such as planning, playing, learning, and extracting knowledge from data. Finally, an afterword draws a parallel between algorithmic problems in operation research and in AI.

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
Author: Csaba Szepesvari
Publsiher: Morgan & Claypool Publishers
Total Pages: 89
Release: 2010
ISBN: 1608454924
Category: Computers
Language: EN, FR, DE, ES & NL

Algorithms for Reinforcement Learning Book Excerpt:

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

Modeling Decisions for Artificial Intelligence

Modeling Decisions for Artificial Intelligence
Author: Vincenc Torra,Yasuo Narukawa,Aïda Valls,Josep Domingo-Ferrer
Publsiher: Springer Science & Business Media
Total Pages: 374
Release: 2006-03-20
ISBN: 3540327800
Category: Computers
Language: EN, FR, DE, ES & NL

Modeling Decisions for Artificial Intelligence Book Excerpt:

This book constitutes the refereed proceedings of the Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, held in Tarragona, Spain, in April 2006. The 31 revised full papers presented together with 4 invited lectures were thoroughly reviewed and selected from 97 submissions. The papers are devoted to theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques.

Emerging Intelligent Computing Technology and Applications With Aspects of Artificial Intelligence

Emerging Intelligent Computing Technology and Applications  With Aspects of Artificial Intelligence
Author: De-Shuang Huang,Kang-Hyun Jo,Hong-Hee Lee,Hee-Jun Kang,Vitoantonio Bevilacqua
Publsiher: Springer
Total Pages: 1120
Release: 2009-09-19
ISBN: 3642040209
Category: Computers
Language: EN, FR, DE, ES & NL

Emerging Intelligent Computing Technology and Applications With Aspects of Artificial Intelligence Book Excerpt:

The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring - gether researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing. ICIC 2009, held in Ulsan, Korea, September 16-19, 2009, constituted the 5th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005 held in Shanghai, Qingdao, Kunming, and Hefei, China, 2008, 2007, 2006, and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the p- ture of contemporary intelligent computing techniques as an integral concept that hi- lights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Emerging Intelligent Computing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.

Cognitive Electronic Warfare An Artificial Intelligence Approach

Cognitive Electronic Warfare  An Artificial Intelligence Approach
Author: Karen Haigh,Julia Andrusenko
Publsiher: Artech House
Total Pages: 288
Release: 2021-07-31
ISBN: 1630818127
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Cognitive Electronic Warfare An Artificial Intelligence Approach Book Excerpt:

This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.

KI 95 Advances in Artificial Intelligence

KI 95  Advances in Artificial Intelligence
Author: Ipke Wachsmuth,Claus Rollinger,Wilfried Brauer
Publsiher: Springer Science & Business Media
Total Pages: 275
Release: 1995-09-04
ISBN: 9783540603436
Category: Computers
Language: EN, FR, DE, ES & NL

KI 95 Advances in Artificial Intelligence Book Excerpt:

This book constitutes the proceedings of the 19th Annual German Conference on Artificial Intelligence, KI-95, held in Bielefeld in September 1995. The volume opens with full versions of four invited papers devoted to the topic "From Intelligence Models to Intelligent Systems". The main part of the book consists of 17 refereed full papers carefully relected by the program committee; these papers are organized in sections on knowledge organization and optimization, logic and reasoning, nonmonotonicity, action and change, and spatial reasoning.

A New Reinforcement Learning Algorithm with Fixed Exploration for Semi Markov Decision Processes

A New Reinforcement Learning Algorithm with Fixed Exploration for Semi Markov Decision Processes
Author: Angelo Michael Encapera
Publsiher: Unknown
Total Pages: 41
Release: 2017
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

A New Reinforcement Learning Algorithm with Fixed Exploration for Semi Markov Decision Processes Book Excerpt:

"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition to the regular iterates of R-SMART, a set of so-called imaging iterates, which form an image of the regular iterates and allow iSMART to avoid exploration decay. The new algorithm is tested extensively on small-scale SMDPs and on large-scale problems from the domain of Total Productive Maintenance (TPM). The algorithm shows encouraging performance on all the cases studied"--Abstract, page iii.

Decision Theory Models for Applications in Artificial Intelligence Concepts and Solutions

Decision Theory Models for Applications in Artificial Intelligence  Concepts and Solutions
Author: Sucar, L. Enrique
Publsiher: IGI Global
Total Pages: 444
Release: 2011-10-31
ISBN: 160960167X
Category: Computers
Language: EN, FR, DE, ES & NL

Decision Theory Models for Applications in Artificial Intelligence Concepts and Solutions Book Excerpt:

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.

KI 2016 Advances in Artificial Intelligence

KI 2016  Advances in Artificial Intelligence
Author: Gerhard Friedrich,Malte Helmert,Franz Wotawa
Publsiher: Springer
Total Pages: 318
Release: 2016-09-08
ISBN: 3319460730
Category: Computers
Language: EN, FR, DE, ES & NL

KI 2016 Advances in Artificial Intelligence Book Excerpt:

This book constitutes the refereed proceedings of the 39th Annual German Conference on Artificial Intelligence, KI 2016, in conjunction with the Österreichische Gesellschaft für Artificial Intelligence, ÖGAI, held in Klagenfurt, Austria, in September 2016. The 8 revised full technical papers presented together with 12 technical communications, and 16 extended abstracts were carefully reviewed and selected from 44 submissions. The conference provides the opportunity to present a wider range of results and ideas that are of interest to the KI audience, including reports about recent own publications, position papers, and previews of ongoing work.

Markov Decision Process

Markov Decision Process
Author: Ward Baird
Publsiher: Unknown
Total Pages: 84
Release: 2021-07-13
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Markov Decision Process Book Excerpt:

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Do you know that you are highly likely part of testing datasets for companies in their machine learning model training, applications, and mobile apps? Don't you want to learn more about the framework that institutions and companies are using for machine learning? The reality is very real. Data collections are everywhere in everything that we do and these behaviors that we exhibit and share willingly with application owners will be used to improve our user experience and improve our daily life such as the usage of Siri, Alexa, Cortana, Google Assistant, and many other applications.

Encyclopedia of Machine Learning

Encyclopedia of Machine Learning
Author: Claude Sammut,Geoffrey I. Webb
Publsiher: Springer Science & Business Media
Total Pages: 1031
Release: 2011-03-28
ISBN: 0387307680
Category: Computers
Language: EN, FR, DE, ES & NL

Encyclopedia of Machine Learning Book Excerpt:

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Handbook on Decision Making

Handbook on Decision Making
Author: Chee Peng Lim
Publsiher: Springer Science & Business Media
Total Pages: 532
Release: 2010-09-07
ISBN: 3642136397
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Handbook on Decision Making Book Excerpt:

Decision making arises when we wish to select the best possible course of action from a set of alternatives. With advancements of the digital technologies, it is easy, and almost instantaneous, to gather a large volume of information and/or data pertaining to a problem that we want to solve. For instance, the world-wi- web is perhaps the primary source of information and/or data that we often turn to when we face a decision making problem. However, the information and/or data that we obtain from the real world often are complex, and comprise various kinds of noise. Besides, real-world information and/or data often are incomplete and ambiguous, owing to uncertainties of the environments. All these make decision making a challenging task. To cope with the challenges of decision making, - searchers have designed and developed a variety of decision support systems to provide assistance in human decision making processes. The main aim of this book is to provide a small collection of techniques stemmed from artificial intelligence, as well as other complementary methodo- gies, that are useful for the design and development of intelligent decision support systems. Application examples of how these intelligent decision support systems can be utilized to help tackle a variety of real-world problems in different - mains, e. g. business, management, manufacturing, transportation and food ind- tries, and biomedicine, are also presented. A total of twenty chapters, which can be broadly divided into two parts, i. e.

Simulation based Algorithms for Markov Decision Processes

Simulation based Algorithms for Markov Decision Processes
Author: Hyeong Soo Chang,Michael C. Fu,Jiaqiao Hu,Steven I. Marcus
Publsiher: Springer Science & Business Media
Total Pages: 189
Release: 2007-05-01
ISBN: 1846286905
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Simulation based Algorithms for Markov Decision Processes Book Excerpt:

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. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
Author: John MacIntyre,Ilias Maglogiannis,Lazaros Iliadis,Elias Pimenidis
Publsiher: Springer
Total Pages: 689
Release: 2019-05-15
ISBN: 3030198235
Category: Computers
Language: EN, FR, DE, ES & NL

Artificial Intelligence Applications and Innovations Book Excerpt:

This book constitutes the refereed proceedings of the 15th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2019, held in Hersonissos, Crete, Greece, in May 2019. The 49 full papers and 6 short papers presented were carefully reviewed and selected from 101 submissions. They cover a broad range of topics such as deep learning ANN; genetic algorithms - optimization; constraints modeling; ANN training algorithms; social media intelligent modeling; text mining/machine translation; fuzzy modeling; biomedical and bioinformatics algorithms and systems; feature selection; emotion recognition; hybrid Intelligent models; classification - pattern recognition; intelligent security modeling; complex stochastic games; unsupervised machine learning; ANN in industry; intelligent clustering; convolutional and recurrent ANN; recommender systems; intelligent telecommunications modeling; and intelligent hybrid systems using Internet of Things. The papers are organized in the following topical sections:AI anomaly detection - active learning; autonomous vehicles - aerial vehicles; biomedical AI; classification - clustering; constraint programming - brain inspired modeling; deep learning - convolutional ANN; fuzzy modeling; learning automata - logic based reasoning; machine learning - natural language; multi agent - IoT; nature inspired flight and robot; control - machine vision; and recommendation systems.