Computational Aspects of Cooperative Game Theory

Author: Georgios Chalkiadakis,Edith Elkind,Michael J. Wooldridge

Publisher: Morgan & Claypool Publishers

ISBN: 1608456528

Category: Computers

Page: 150

View: 6722

DOWNLOAD NOW »

This cross-disciplinary book dives into the technical and computational aspects that make cooperative games possible. It is appropriate for professional researchers, graduate students, and advanced undergraduates hoping to pursue careers in academia and / or industry.

Essentials of Game Theory

A Concise Multidisciplinary Introduction

Author: Kevin Leyton-Brown,Yoav Shoham

Publisher: Morgan & Claypool Publishers

ISBN: 1598295942

Category: Computers

Page: 88

View: 8406

DOWNLOAD NOW »

Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This Synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them.

Computing and Combinatorics

21st International Conference, COCOON 2015, Beijing, China, August 4-6, 2015, Proceedings

Author: Dachuan Xu,Donglei Du,Dingzhu Du

Publisher: Springer

ISBN: 3319213989

Category: Computers

Page: 785

View: 436

DOWNLOAD NOW »

This book constitutes the refereed proceedings of the 21st International Conference on Computing and Combinatorics, COCOON 2015, held in Beijing, China, in August 2015. The 49 revised full papers and 11 shorter papers presented were carefully reviewed and selected from various submissions. The papers cover various topics including algorithms and data structures; algorithmic game theory; approximation algorithms and online algorithms; automata, languages, logic and computability; complexity theory; computational learning theory; cryptography, reliability and security; database theory, computational biology and bioinformatics; computational algebra, geometry, number theory, graph drawing and information visualization; graph theory, communication networks, optimization and parallel and distributed computing.

Artificial Intelligence and Games

Author: Georgios N. Yannakakis,Julian Togelius

Publisher: Springer

ISBN: 3319635190

Category: Computers

Page: 337

View: 6842

DOWNLOAD NOW »

This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.

LATIN 2014: Theoretical Informatics

11th Latin American Symposium, Montevideo, Uruguay, March 31 -- April 4, 2014. Proceedings

Author: Alberto Pardo,Alfredo Viola

Publisher: Springer

ISBN: 3642544231

Category: Computers

Page: 767

View: 5660

DOWNLOAD NOW »

This book constitutes the refereed proceedings of the 11th Latin American Symposium on Theoretical Informatics, LATIN 2014, held in Montevideo, Uruguay, in March/April 2014. The 65 papers presented together with 5 abstracts were carefully reviewed and selected from 192 submissions. The papers address a variety of topics in theoretical computer science with a certain focus on complexity, computational geometry, graph drawing, automata, computability, algorithms on graphs, algorithms, random structures, complexity on graphs, analytic combinatorics, analytic and enumerative combinatorics, approximation algorithms, analysis of algorithms, computational algebra, applications to bioinformatics, budget problems and algorithms and data structures.

A Course on Cooperative Game Theory

Author: Satya R. Chakravarty,Manipushpak Mitra,Palash Sarkar

Publisher: Cambridge University Press

ISBN: 1107058791

Category: Business & Economics

Page: 273

View: 9872

DOWNLOAD NOW »

"Deals with real life situations where objectives of the participants are partially cooperative and partially conflicting"--

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

Author: Nikos Vlassis

Publisher: Morgan & Claypool Publishers

ISBN: 1598295268

Category: Computers

Page: 71

View: 8565

DOWNLOAD NOW »

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

Multiagent Systems

Algorithmic, Game-Theoretic, and Logical Foundations

Author: Yoav Shoham,Kevin Leyton-Brown

Publisher: Cambridge University Press

ISBN: 113947524X

Category: Computers

Page: N.A

View: 4816

DOWNLOAD NOW »

Multiagent systems combine multiple autonomous entities, each having diverging interests or different information. This overview of the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate or graduate courses. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming.

Fun and Games

A Text on Game Theory

Author: K. G. Binmore

Publisher: D C Heath & Company

ISBN: 9780669246032

Category: Business & Economics

Page: 602

View: 4051

DOWNLOAD NOW »

Binmore' s groundbreaking text on game theory explores the manner in which rational people should interact when they have conflicting interests. While Binmore uses a light touch to outline key developments in theory, the text remains a serious exposition of a serious topic. In addition, his unique story-telling approach allows students to immediately apply game-theoretic skills to simple problems. Each chapter ends with a host of challenging exercises to help students practice the skills they have learned. The highly anticipated revision, expected in 2003, will include more coverage of cooperative game theory and a more accessible presentation--with chapters broken up into smaller chunks and an abundance of economic examples integrated throughout the text.

Robot Learning from Human Teachers

Author: Sonia Chernova,Andrea L. Thomaz

Publisher: Morgan & Claypool Publishers

ISBN: 1681731797

Category: Computers

Page: 121

View: 8201

DOWNLOAD NOW »

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.

A Concise Introduction to Models and Methods for Automated Planning

Author: Hector Geffner,Blai Bonet

Publisher: Morgan & Claypool Publishers

ISBN: 1608459705

Category: Computers

Page: 141

View: 3967

DOWNLOAD NOW »

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography

Handbook of Computational Social Choice

Author: Felix Brandt,Vincent Conitzer,Ulle Endriss,Jérôme Lang,Ariel D. Procaccia

Publisher: Cambridge University Press

ISBN: 1316489752

Category: Computers

Page: N.A

View: 2660

DOWNLOAD NOW »

The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.

Game Theory for Data Science

Eliciting Truthful Information

Author: Boi Faltings,Goran Radanovic

Publisher: Morgan & Claypool Publishers

ISBN: 1681731959

Category: Computers

Page: 151

View: 8431

DOWNLOAD NOW »

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.

Predicting Human Decision-Making

From Prediction to Action

Author: Ariel Rosenfeld,Sarit Kraus

Publisher: Morgan & Claypool Publishers

ISBN: 1681732750

Category: Computers

Page: 150

View: 1399

DOWNLOAD NOW »

Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

Case-based Reasoning

A Concise Introduction

Author: Beatriz Lopez

Publisher: Morgan & Claypool Publishers

ISBN: 1627050078

Category: Computers

Page: 87

View: 1034

DOWNLOAD NOW »

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the designing of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage future students to solve current CBR challenges.

Adaptation in Natural and Artificial Systems

An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

Author: John Henry Holland

Publisher: MIT Press

ISBN: 9780262581110

Category: Psychology

Page: 211

View: 3251

DOWNLOAD NOW »

List of figures. Preface to the 1992 edition. Preface. The general setting. A formal framework. lustrations. Schemata. The optimal allocation of trials. Reproductive plans and genetic operators. The robustness of genetic plans. Adaptation of codings and representations. An overview. Interim and prospectus. Glossary of important symbols.

Multi-Objective Decision Making

Author: Diederik M. Roijers,Shimon Whiteson

Publisher: Morgan & Claypool Publishers

ISBN: 1681731827

Category: Computers

Page: 129

View: 4470

DOWNLOAD NOW »

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.

Answer Set Solving in Practice

Author: Martin Gebser,Roland Kaminski,Benjamin Kaufmann

Publisher: Morgan & Claypool Publishers

ISBN: 1608459713

Category: Computers

Page: 240

View: 9944

DOWNLOAD NOW »

Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples

The Design of Innovation

Lessons from and for Competent Genetic Algorithms

Author: David E. Goldberg

Publisher: Springer Science & Business Media

ISBN: 1475736436

Category: Computers

Page: 248

View: 5301

DOWNLOAD NOW »

7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of Inter-BB Difficulty: Scaling 83 7 The Core of Extra-BB Difficulty: Noise 88 Crosstalk: All Roads Lead to the Core 8 89 9 From Multimodality to Hierarchy 93 10 Summary 100 7 ENSURING BUILDING BLOCK SUPPLY 101 1 Past Work 101 2 Facetwise Supply Model I: One BB 102 Facetwise Supply Model II: Partition Success 103 3 4 Population Size for BB Supply 104 Summary 5 106 8 ENSURING BUILDING BLOCK GROWTH 109 1 The Schema Theorem: BB Growth Bound 109 2 Schema Growth Somewhat More Generally 111 3 Designing for BB Market Share Growth 112 4 Selection Press ure for Early Success 114 5 Designing for Late in the Day 116 The Schema Theorem Works 6 118 A Demonstration of Selection Stall 7 119 Summary 122 8 9 MAKING TIME FOR BUILDING BLOCKS 125 1 Analysis of Selection Alone: Takeover Time 126 2 Drift: When Selection Chooses for No Reason 129 3 Convergence Times with Multiple BBs 132 4 A Time-Scales Derivation of Critical Locus 142 5 A Little Model of Noise-Induced Run Elongation 143 6 From Alleles to Building Blocks 147 7 Summary 148 10 DECIDING WELL 151 1 Why is Decision Making a Problem? 151