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

International Journal of Computer and Applications

This chapter is devoted to a more detailed examination of game theory. Game theory is an important tool for analyzing strategic behavior, is concerned with how individuals make decisions when they recognize that their actions affect, and are affected by, the actions of other individuals or groups. Strategic behavior recognizes that the decision-making process is frequently mutually interdependent. Game theory is the study of the strategic behavior involving the interaction of two or more individuals, teams, or firms, usually referred to as players. Two game theoretic scenarios were examined in this chapter: Simultaneous-move and multi-stage games. In simultaneous-move games the players effectively move at the same time. A normal-form game summarizes the players, possible strategies and payoffs from alternative strategies in a simultaneous-move game. Simultaneous-move games may be either noncooperative or cooperative. In contrast to noncooperative games, players of cooperative games engage in collusive behavior. A Nash equilibrium, which is a solution to a problem in game theory, occurs when the players’ payoffs cannot be improved by changing strategies. Simultaneous-move games may be either one-shot or repeated games. One-shot games are played only once. Repeated games are games that are played more than once. Infinitely-repeated games are played over and over again without end. Finitely-repeated games are played a limited number of times. Finitely-repeated games have certain or uncertain ends.

An Introduction to Game Theory

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Information Security in Big Data: Privacy and Data Mining

Indoleamine 2,3-dioxygenase (IDO) is emerging as an important new therapeutic drug target for the treatment of cancer characterized by pathological immune suppression IDO catalyzes the rate-limiti

Molecular docking and dynamic simulation studies evidenced plausible immunotherapeutic anticancer property by Withaferin A targeting indoleamine 2,3-dioxygenase

One of the most crucial tool for network defense is an Intrusion Detection Systems (IDS). Several supervised machine learning algorithms exist that deal with high volumes of training data. However, there is a need to design optimized, low cost semi-supervised IDS model that can perform well with little labelled data. The challenge is also to design adaptive learning algorithms that can update themselves with minimum computational overhead in case new training data is to be included. This work demonstrates a hybrid semi-supervised machine learning technique that uses Active learning Support Vector Machine (ASVM) and Fuzzy C-Means (FCM) clustering in the design of an efficient IDS. This algorithm is tested on NSL KDD bench mark IDS data set and found to be promising.

A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering

https://isiarticles.com/bundles/Article/pre/pdf/29147.pdf

Intrusion Detection System using Bayesian Network and Hidden Markov Model

In the process of detecting different kinds of attacks in anomaly-based intrusion detection system (IDS), both normal and attack data are profiled with the help of selected attributes. Various types of attributes are collected to create the attack and normal traffic patterns. Some of the attributes are derived from protocol header fields, and some of them represent continuous information profiled over a period. “Curse of Dimensionality” is one of the major issues in IDS. The computational complexity of the model generation and classification time of IDS is directly proportional to the number of attributes of the profile. In a typical IDS preprocessing stage, more significant features among the available features are selected. This paper presents a brief taxonomy of several feature selection methods with emphasis on soft computing techniques, viz., rough sets, fuzzy rough sets, and ant colony optimization.

A Survey of Feature Selection Techniques in Intrusion Detection System: A Soft Computing Perspective

Summary Data privacy is the most acclaimed problem when publishing individual data. It ensures individual data publishing without disclosing sensitive data. The much popular approach, is K-Anonymity, where data is transformed to equivalence classes, each class having a set of K- records that are indistinguishable from each other. But several authors have pointed out numerous problems with K-anonymity and have proposed techniques to counter them or avoid them. l-diversity and t-closeness are such techniques to name a few. Our study has shown that all these techniques increase computational effort to practically infeasible levels, though they increase privacy. A few techniques account for too much of information loss, while achieving privacy. In this paper, we propose a novel, holistic approach for achieving maximum privacy with no information loss and minimum overheads (as only the necessary tuples are transformed). We address the data privacy problem using fuzzy set approach, a total paradigm shift and a new perspective of looking at privacy problem in data publishing. Our practically feasible method in addition, allows personalized privacy preservation, and is useful for both numerical and categorical attributes.

/pdf/fuzzy-based-approach-for-privacy-preserving-publication-of-40pvhebpig.pdf

V. Valli Kumari

Papers

Molecular docking and dynamic simulation studies evidenced plausible immunotherapeutic anticancer property by Withaferin A targeting indoleamine 2,3-dioxygenase

A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering

Intrusion Detection System using Bayesian Network and Hidden Markov Model

A Survey of Feature Selection Techniques in Intrusion Detection System: A Soft Computing Perspective

Fuzzy based approach for privacy preserving publication of data