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Game Theory and Decision Theory in Agent-Based Systems
Author: Simon D. Parsons
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 403
ISBN-13: 1461511070
DOWNLOAD EBOOK →Game Theory And Decision Theory In Agent-Based Systems is a collection of papers from international leading researchers, that offers a broad view of the many ways game theory and decision theory can be applied in agent-based systems, from standard applications of the core elements of the theory to more cutting edge developments. The range of topics discussed in this book provide the reader with the first comprehensive volume that reflects both the depth and breadth of work in applying techniques from game theory and decision theory to design agent-based systems. Chapters include: Selecting Partners; Evolution of Agents with Moral Sentiments in an IPD Exercise; Dynamic Desires; Emotions and Personality; Decision-Theoretic Approach to Game Theory; Shopbot Economics; Finding the Best Way to Join in; Shopbots and Pricebots in Electronic Service Markets; Polynomial Time Mechanisms; Multi-Agent Q-learning and Regression Trees; Satisficing Equilibria; Investigating Commitment Flexibility in Multi-agent Contracts; Pricing in Agent Economies using Multi-agent Q-learning; Using Hypergames to Increase Planned Payoff and Reduce Risk; Bilateral Negotiation with Incomplete and Uncertain Information; Robust Combinatorial Auction Protocol against False-name Bids.
Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality
Author: Jianye Hao
Publisher: Springer
Published: 2016-04-13
Total Pages: 184
ISBN-13: 3662494701
DOWNLOAD EBOOK →This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.
A Game Theoretic Framework for Communication Decisions in Multi Agent Systems
Author: Tummalapalli Sudhamsh Reddy
Publisher:
Published: 2012
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →Communication is the process of transferring information between multiple enti- ties. We study the communication process between di erent entities that are modeled as multiple agents. Agents are assumed to be rational and take actions to increase their utility. To study the interactions between these entities we use game theory, which is a mathematical tool that is used to model the interactions and decision process of these multiple players/agents. In the presence of multiple players their interactions are generally modelled as a stochastic game. In most cases it is clear that communication can help in coordinating the actions between multiple agents such that they can achieve higher utility, but, what is not clear is how the agents can take decisions about when to communicate and more importantly what to communicate. In this dissertation, we focus on the question of what information the agents can communicate and how they take decisions on selecting information to communicate. Here we assume that the communication medium, protocols and language are already present in this multiagent system. We address the question of information selection in the communication process. In this thesis, we develop a formal framework for communication between dif- ferent agents using game theory. Our major contributions are: A classi cations of multiagent systems and what information to communicate in these various cases. Algorithms for inverse reinforcement learning in multiagent systems, which al- low an agent to get a better understanding about the other agents. A mathematical framework using which the agents can make two important decisions, when to communicate, and, more importantly what to communicate in di erent classes of multiagent systems.
A Game Theoretic Approach to Multi-agent Systems in Highly Dynamic, Information-sparse, Role Assignment Scenarios
Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Author: Tatiana Tatarenko
Publisher: Springer
Published: 2017-09-19
Total Pages: 176
ISBN-13: 3319654799
DOWNLOAD EBOOK →This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.
A Game Theoretic Approach to Normative Multi-agent Systems
Multiagent Systems
Author: Yoav Shoham
Publisher: Cambridge University Press
Published: 2008-12-15
Total Pages: 504
ISBN-13: 9780521899437
DOWNLOAD EBOOK →This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics. 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. Written by two of the leading researchers of this engaging field, this book will surely serve as THE reference for researchers in the fastest-growing area of computer science, and be used as a text for advanced undergraduate or graduate courses.
Game Theoretic Control of Multi-agent Systems
Coevolutionary Game-theoretic Multi-agent Systems
