Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the state and model variables. Our empirical results show that this method outperforms a number of BRL baselines and is able to learn efficiently when the factorization is known, as well as learn both the factorization and the model parameters simultaneously.

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Bayesian Reinforcement Learning in Factored POMDPs

Agent software is a rapidly developing area of research. However, the overuse of the word ‘agent’ has tended to mask the fact that, in reality, there is a truly heterogeneous body of research being carried out under this banner. This overview paper presents a typology of agents. Next, it places agents in context, defines them and then goes on, inter alia, to overview critically the rationales, hypotheses, goals, challenges and state-of-the-art demonstrators of the various agent types in our typology. Hence, it attempts to make explicit much of what is usually implicit in the agents literature. It also proceeds to overview some other general issues which pertain to all the types of agents in the typology. This paper largely reviews software agents, and it also contains some strong opinions that are not necessarily widely accepted by the agent community.

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Software agents: an overview

Multiagent systems are made up of multiple interacting intelligent agents -- computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care This book offers a state-of-the-art introduction to multiagent systems, covering the field in both breadth and depth, and treating both theory and practice It is suitable for classroom use or independent study This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999 Sixteen of the book's seventeen chapters were written for this edition; all chapters are by leaders in the field, with each author contributing to the broad base of knowledge and experience on which the book rests The book covers basic concepts of computational agency from the perspective of both individual agents and agent organizations; communication among agents; coordination among agents; distributed cognition; development and engineering of multiagent systems; and background knowledge in logics and game theory Each chapter includes references, many illustrations and examples, and exercises of varying degrees of difficulty The chapters and the overall book are designed to be self-contained and understandable without additional material Supplemental resources are available on the book's Web site Contributors:Rafael Bordini, Felix Brandt, Amit Chopra, Vincent Conitzer, Virginia Dignum, Jurgen Dix, Ed Durfee, Edith Elkind, Ulle Endriss, Alessandro Farinelli, Shaheen Fatima, Michael Fisher, Nicholas R Jennings, Kevin Leyton-Brown, Evangelos Markakis, Lin Padgham, Julian Padget, Iyad Rahwan, Talal Rahwan, Alex Rogers, Jordi Sabater-Mir, Yoav Shoham, Munindar P Singh, Kagan Tumer, Karl Tuyls, Wiebe van der Hoek, Laurent Vercouter, Meritxell Vinyals, Michael Winikoff, Michael Wooldridge, Shlomo Zilberstein

Multiagent Systems

Part 1 Machines that make deals: the premise machine encounters social engineering for machines scenarios how does this differ from Al? how does this differ from game theory? Part 2 Interaction mechanisms: the negotiation problem in different domains attributes of negotiation mechanisms assumptions incentive compatibility Part 3 Task-oriented domains: domain definition attributes and examples a negotiation mechanism evaluation of the negotiation mechanism an alternative, one-step protocol mechanisms that maximize the product of utilities the bottom line Part 4 Deception-free protocols: non-manipulable negotiation mechanisms probabilistic deals subadditive domains concave domains modular domains summary of incentive compatible mechanisms the bottom line Part 5 State-oriented domains: side-effects in encounters domain definition attributes and examples a negotiation mechanism worth of a goal conflict resolution semi-co-operative deals in non-conflict situations unified negotiation protocols (UNP) multi-plan deals the hierarchy of deal types - summary unbounded worth of a goal - tidy agents the bottom line Part 6 Strategic manipulation: negotiation with incomplete information incomplete information about worth of goals using the revelation principle to re-design the mechanisms the bottom line Part 7 Worth-oriented domains: goal relaxation domain definition one agent best plan negotiation over sub-optimal states examples of worth functions the bottom line Appendices: strict/tolerant mechanisms some related work proofs

Rules of encounter: designing conventions for automated negotiation among computers

Multiagent negotiation under time constraints

Negotiation among multiple agents remains an important topic of research in Distributed Artificial Intelligence (DAI). Most previous work on this subject, however, has focused on bilateral negotiation, deals that are reached between two agents. There has also been research on n-agent agreement which has considered "consensus mechanisms" (such as voting), that allow the full group to coordinate itself. These group decision-making techniques, however, assume that the entire group will (or has to) coordinate its actions. Sub-groups cannot make sub-agreements that exclude other members of the group.

In some domains, however, it may be possible for beneficial agreements to be reached among sub-groups of agents, who might be individually motivated to work together to the exclusion of others outside the group. This paper considers this more general case of n-agent coalition formation. We present a simple coalition formation mechanism that uses cryptographic techniques for subadditive Task Oriented Domains. The mechanism is efficient, symmetric, and individual rational. When the domain is also concave, the mechanism also satisfies coalition rationality.

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Coalition, Cryptography, and Stability: Mechanisms for Coalition Formation in Task Oriented Domains

Research in Distributed Artificial Intelligence is concerned with how automated agents can be designed to interact effectively. One important capability that could aid inter-agent cooperation would be that of negotiation: agents could be built that are able to communicate their respective desires and compromise to reach mutually beneficial agreements.

This work uses the language of game theory to analyze negotiation among automated agents in cooperative domains. However, while game theory generally deals with negotiation in continuous domains and among agents with full information, this research considers discrete domains and the case where agents have only partial information, assumptions of greater interest for artificial intelligence.

A novel, stable, negotiation protocol is introduced for the case of agents who are able to share a discrete set of tasks with one another. The case of agents who may lie to one another during the negotiation, either by hiding some of their tasks or by creating fictitious tasks, is analyzed; it is shown that under some conditions lies are beneficial and "safe," i.e., undiscoverable, while under other circumstances, lies can never be safe.

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Negotiation and task sharing among autonomous agents in cooperative domains

As distributed systems of computers play an increasingly important role in society, it will be necessary to consider ways in which these machines can be made to interact effectively. We are concerned with heterogeneous, distributed systems made up of machines that have been programmed by different entities to pursue different goals. Adjusting the rules of public behavior (the rules of the game) by which the programs must interact can influence the private strategies that designers set up in their machines. These rules can shape the design choices of the machines' programmers and, thus, the run-time behavior of their creations. Certain kinds of desirable social behavior can thus be caused to emerge through the careful design of interaction rules. Formal tools and analysis can help in the appropriate design of these rules. We consider how concepts from fields such as decision theory and game theory can provide standards to be used in the design of appropriate negotiation and interaction environments. This design is highly sensitive to the domain in which the interaction is taking place. This article is adapted from an invited lecture given by Jeffrey Rosenschein at the Thirteenth International Joint Conference on Artificial Intelligence in Chambery, France, on 2 September 1993.

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Gilad Zlotkin

Papers

Rules of encounter: designing conventions for automated negotiation among computers

Multiagent negotiation under time constraints

Coalition, Cryptography, and Stability: Mechanisms for Coalition Formation in Task Oriented Domains

Negotiation and task sharing among autonomous agents in cooperative domains

Designing conventions for automated negotiation