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

비만아 부모의 돌봄 경험: q 방법론

An Introduction to MultiAgent Systems.

Slime mould algorithm: A new method for stochastic optimization

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

/pdf/self-adaptive-software-landscape-and-research-challenges-4rt6vhdmq2.pdf

Self-adaptive software: Landscape and research challenges

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

/pdf/biogeography-based-learning-particle-swarm-optimization-5e0wus1gyg.pdf

Biogeography-based learning particle swarm optimization

https://www.cse.msu.edu/~chengb/CSE891/Handouts/autonomic-computing-concise-intro-Sterritt.pdf

A concise introduction to autonomic computing

This paper presents a comprehensive study on the challenges and issues of security in cloud computing. We first look into the impacts of the distinctive characteristics of cloud computing, namely, multi-tenancy, elasticity and third party control, upon the security requirements. Then, we analyze the cloud security requirements in terms of the fundamental issues, i.e., confidentiality, integrity, availability, trust, and audit and compliance. Furthermore, we discuss the taxonomy for security issues in cloud computing. Finally, we summarize the security issues in cloud computing by a cloud security architecture.

Security issues in cloud computing

Keynotes.- Software Agents: The Future of Web Services.- Building Automated Negotiators.- Emergence in Cyberspace: Towards the Evolutionary Self-Organizing Enterprise.- Regular Papers.- Requirements Analysis in Tropos: A Self-Referencing Example.- A Mechanism for Dynamic Role Playing.- Agent UML Class Diagrams Revisited.- The Behavior-Oriented Design of Modular Agent Intelligence.- Engineering JADE Agents with the Gaia Methodology.- Designing Peer-to-Peer Applications: An Agent-Oriented Approach.- Introducing Pattern Reuse in the Design of Multi-agent Systems.- Specifying Reuse Concerns in Agent System Design Using a Role Algebra.- Comparison of Some Negotiation Algorithms Using a Tournament-Based Approach.- State-Based Modeling Method for Multiagent Conversation Protocols and Decision Activities.- A Framework for Inter-society Communication in Agents.- Action Recognition and Prediction for Driver Assistance Systems Using Dynamic Belief Networks.- Collaborative Agent System Using Fuzzy Logic for Optimisation.- A Self-Organizational Management Network Based on Adaptive Resonance Theory.- Mobile Software Agents for Location-Based Systems.- Partner Detection and Selection in Emergent Holonic Enterprises.- A Multi-agent System for E-insurance Brokering.- An XML Multi-agent System for E-learning and Skill Management.- Integrating Mobile and Intelligent Agents in Advanced E-commerce: A Survey.- An Agent-Oriented Approach to Industrial Automation Systems.- Multi-agent Model to Control Production System: A Reactive and Emergent Approach by Cooperation and Competition between Agents.- Integrating Agents in Software Applications.- A Foundational Analysis of Software Robustness Using Redundant Agent Collaboration.

Agent technologies, infrastructures, tools, and applications for e-services : NODe 2002 Agent-Related Workshops, Erfurt, Germany, October 7-10, 2002 : revised papers

Resource over provisioning in cloud computing consumes energy excessively. Energy-aware dynamic virtual machine consolidation (DVMC) reduces energy consumption without compromising service level agreement. In this paper, we put forward a new framework of DVMC for green cloud computing. In particular, we propose a new virtual machine (VM) placement policy, namely, space aware best fit decreasing (SABFD) and a new migration VM selection policy, namely, high CPU utilization-based migration VM selection (called HS). Thorough simulations are carried out to evaluate the performances of different energy-aware DVMC plans based on real-world workload traces, with DVMC plans as various combinations of host overload detection, migration VM selection, and VM placement policies. The simulation results show that DVMC plans with SABFD policy or with HS policy outperforms alternative DVMC plans. What is more, a DVMC plan with both SABFD and HS policies makes the best performance.

/pdf/energy-aware-dynamic-virtual-machine-consolidation-for-cloud-pkt3uzozls.pdf

Huaglory Tianfield

Papers

Biogeography-based learning particle swarm optimization

A concise introduction to autonomic computing

Security issues in cloud computing

Agent technologies, infrastructures, tools, and applications for e-services : NODe 2002 Agent-Related Workshops, Erfurt, Germany, October 7-10, 2002 : revised papers

Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters