Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

物件導向軟體之架構(Object-Oriented Software Construction)探討

Social Network Analysis

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

An Introduction to MultiAgent Systems.

Although the benefits of requirements traceability are widely recognized, the actual practice of maintaining a traceability scheme is not always entirely successful. The traceability infrastructure underlying a software system tends to erode over its lifetime, as time-pressured practitioners fail to consistently maintain links and update impacted artifacts each time a change occurs, even with the support of automated systems. This paper proposes a new method of traceability based upon event-notification and is applicable even in a heterogeneous and globally distributed development environment. Traceable artifacts are no longer tightly coupled but are linked through an event service, which creates an environment in which change is handled more efficiently, and artifacts and their related links are maintained in a restorable state. The method also supports enhanced project management for the process of updating and maintaining the system artifacts.

Event-based traceability for managing evolutionary change

Changes and advancements in technology have the potential to benefit older adults by promoting independence and increasing the ability to age in place. However, older adults are less likely to adopt new technology unless they see benefits to themselves. This study assessed the perceptions of 30 older adults in the Midwest concerning technology via three separate focus groups (i.e., independent apartment complex, a rural community, exercise program participants), which addressed a need in the literature (i.e., inclusion of oldest-old and rural individuals). The focus group questions included items such as what technology older adults currently used, desired improvements in technology, and the greatest challenges participants were facing or would face in the future. Overall, older adults were enthusiastic about learning new forms of technology that could help them maintain their independence and quality of life. Five themes emerged from all three focus groups: (a) Frustrations, Limitations, and Usability Concerns; (b) Transportation; (c) Help and Assistance; (d) Self-Monitoring; and (e) Gaming. The themes have important implications for future technology developed for older adults; in particular, older adults were willing and eager to adopt new technology when usefulness and usability outweighed feelings of inadequacy.

Perceptions of technology among older adults.

The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions, either optimal or near optimal, must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management (PM) techniques, commercial PM tools, and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience, must build the project database to record such decisions and represent them as project nets, then use the tools to track progress, perform simple consistency checks, analyze the project net for critical paths, etc., and produce reports in various formats such as Gantt or Pert charts.

Our research has developed a new technique based on genetic algorithms (GA) that automatically determines, using a programmable goal function, a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based on the results of these algorithms, the software manager will be able to assign tasks to staff in an optimal manner and predict the corresponding future status of the project, including an extensive analysis on the time-and-cost variations in the solution space. Our experiments utilized Wall's GALib as the search engine. The algorithms operated on a richer, refined version of project management networks derived from Chao's seminal work on GA-based Software Project Management Net (SPMnet). Generalizing the results of Chao's solution, the new GA algorithms can operate on much more complex scheduling networks involving multiple projects. They also can deal with more realistic programmatic and organizational assumptions. The results of the GA algorithm were evaluated using exhaustive search for five test cases. In these tests our GA showed strong scalability and simplicity. Its orthogonal genetic form and modularized heuristic functions are well suited for complex conditional optimization problems, of which project management is a typical example.

/pdf/genetic-algorithms-for-project-management-4jnvibb8td.pdf

Genetic Algorithms for Project Management

Service-oriented architecture (SOA) provides a scalable and flexible framework for service composition. Service composition algorithms play an important role in selecting services from different providers to reach desirable QoS levels according to the performance requirements of composite services, and improve customer satisfaction. This paper proposes a novel QoS-based dynamic service composition technique for web services with Ant Colony Optimization (ACO) in an optimization approach. The novelty of this work lies with our multi-objective optimal-path selection modeling for QoS-based dynamic web service composition and a new version of ACO algorithm that is proposed to solve this multi-objective optimization problem. The experiments show that the new version of ACO algorithm is very efficient in solving such a problem.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNxV4ixV/pdf

QoS-Based Dynamic Web Service Composition with Ant Colony Optimization

Effective management of complex software projects depends on the ability to solve complex, subtle optimization problems. Most studies on software project management do not pay enough attention to difficult problems such as employee-to-task assignments, which require optimal schedules and careful use of resources. Commercial tools, such as Microsoft Project, assume that managers as users are capable of assigning tasks to employees to achieve the efficiency of resource utilization, while the project continually evolves. Our earlier work applied genetic algorithms (GAs) to these problems. This paper extends that work, introducing a new, richer model that is capable of more realistically simulating real-world situations. The new model is described along with a new GA that produces optimal or near-optimal schedules. Simulation results show that this new model enhances the ability of GA-based approaches, while providing decision support under more realistic conditions.

Carl K. Chang

Papers

Event-based traceability for managing evolutionary change

Perceptions of technology among older adults.

Genetic Algorithms for Project Management

QoS-Based Dynamic Web Service Composition with Ant Colony Optimization

Time-line based model for software project scheduling with genetic algorithms