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

The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.

Numerical methods for engineers

Probability, Reliability and Statistical Methods in Engineering Design

Functionally graded materials (FGMs) are a broad research area and attract considerable tremendous attention today in the materials science and engineering society. In recent years, FGMs have experienced remarkable developments in manufacturing methods. FGMs can be produced using several well-known processing techniques from conventional to advanced. This research provides an overview of manufacturing methods for FGMs, thus describing the fundamental difficulties and strengths of these methods based on the available literature over 30 years. Besides, this critical review gives a comprehensive summary of the various applications and the future trends for research required for the design and production of these materials properly with a smooth graded. The anticipated findings of the current paper can be considered as a milestone for future production and analysis investigations for FGMs and are beneficial for researchers, designers, and manufacturers in this scope.

30 Years of functionally graded materials: An overview of manufacturing methods, Applications and Future Challenges

Quality and productivity methods for evaluating quality parameter design methods for specifying tolerance quality management for production processes

Taguchi on robust technology development : bringing quality engineering upstream

Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

Fused filament fabrication (FFF) is one of the most popular additive manufacturing (AM) processes that utilize thermoplastic polymers to produce three-dimensional (3D) geometry products. The FFF filament materials have a significant role in determining the properties of the final part produced, such as mechanical properties, thermal conductivity, and electrical conductivity. This article intensively reviews the state-of-the-art materials for FFF filaments. To date, there are many different types of FFF filament materials that have been developed. The filament materials range from pure thermoplastics to composites, bioplastics, and composites of bioplastics. Different types of reinforcements such as particles, fibers, and nanoparticles are incorporated into the composite filaments to improve the FFF build part properties. The performance, limitations, and opportunities of a specific type of FFF filament will be discussed. Additionally, the challenges and requirements for filament production from different materials will be evaluated. In addition, to provide a concise review of fundamental knowledge about the FFF filament, this article will also highlight potential research directions to stimulate future filament development. Finally, the importance and scopes of using bioplastics and their composites for developing eco-friendly filaments will be introduced.

A Review on Filament Materials for Fused Filament Fabrication

Fused deposition modeling (FDM) is one of the most widely used additive manufacturing processes to produce prototypes as well as functional parts from thermoplastics. However, the applications of the FDM process are still limited due to several drawbacks, such as poor surface quality, poor mechanical properties, or high built time. It has been deemed that the part characteristics and build time can be improved by determining an optimum combination of process parameters. To optimize multiple process parameters in FDM, this paper employs a multi-objective particle swarm optimization based on the data collected from the experimental study. Four (04) process parameters, namely layer thickness, build orientation, infill density, and extrusion temperature are optimized to achieve higher compressive strength and lower build time. The optimization results provide information on the combined impacts of the four process parameters on compressive strength and build time. This information can aid decision makers with better judgment when dealing with multiple conflicting objectives.

Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization

A robust optimization approach for solving two-person games under interval uncertainty

Fused deposition modeling (FDM) based three-dimensional (3D) printing process has become a popular and economical alternative to produce complex 3D structures without the necessity of expensive tooling. There are a set of process parameters involved in the FDM process that should be appropriately set to ensure the desired 3D part properties are achieved. The relationships between these process parameters are often complicated, conflicting, and interdependent. The selection of optimum process parameters combination is essential. Many approaches can be taken to optimize FDM process parameters. Although experimental methods are exhaustive, they are often expensive and time-consuming. Alternatively, numerical methods have gained a lot of traction in the application of optimizing FDM process parameters. This chapter will introduce multi-objective optimization and review the state-of-the-art optimization methods based on evolutionary algorithms that can be employed in optimizing FDM process parameters. Multi-objective optimization involves optimizing more than one objective function simultaneously and often results in many optimal solutions known as Pareto-optimal solutions. This Pareto-optimal set of solutions can help decision-makers make a robust optimal selection for FDM process parameters.

Arup Dey

Papers

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

A Review on Filament Materials for Fused Filament Fabrication

Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization

A robust optimization approach for solving two-person games under interval uncertainty

Multi-Objective Optimization for FDM Process Parameters with Evolutionary Algorithms