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

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

: Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software.

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Feature-Oriented Domain Analysis (FODA) Feasibility Study

語用論(Pragmatics)を考える

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

/pdf/retrieval-reuse-revision-and-retention-in-case-based-51qpcadmkh.pdf

Retrieval, reuse, revision and retention in case-based reasoning

Over the last decade research into design processes utilizing ideas and models drawn from artificial intelligence has resulted in a better understanding of design -- particularly routine design -- as a process. Indeed, most of the current research activity directly or indirectly deals only with routine design. Not surprisingly, many practicing designers state that the level of understanding represented by these models is only of mild interest because they fail to embody any ideas about creativity. This volume provides a set of chapters in the areas of modeling creativity and knowledge-based creative design that examines the potential role and form of computer-aided design which supports creativity. It aims to define the state-of-the-art of computational creativity in design as well as to identify research directions. Published at a time when the field of computational creativity in design is still immature, it should influence the directions of growth and assist the field in reaching maturity.

Modeling Creativity and Knowledge-Based Creative Design

Most computer-based design tools assume designers work with a well defined problem. However, this assumption has been challenged by current research. The explorative aspect of design, especially during conceptual design, is not fully addressed. This paper introduces a model for problem-design exploration, and how this model can be implemented using the genetic algorithm (GA) paradigm. The basic GA, which does not support our exploration model, evaluates individuals from a population of design solutions with an unchanged fitness function. This approach to evaluation implements search with a prefixed goal. Modifications to the basic GA are required to support exploration. Two approaches to implement a co-evolving GA are presented and discussed in this paper: one in which the fitness function is represented within the genotype, and a second in which the fitness function is modelled as a separately evolving population of genotypes.

Modeling Design Exploration as Co-Evolution

Case-Based Reasoning in Design

Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions The study shows that co-evolutionary design is a good cognitive model of design and highlights the similarities and differences between the computational model and the cognitive model The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process

https://ir.nctu.edu.tw:443/bitstream/11536/28133/1/000181594500005.pdf

Mary Lou Maher

Papers

Retrieval, reuse, revision and retention in case-based reasoning

Modeling Creativity and Knowledge-Based Creative Design

Modeling Design Exploration as Co-Evolution

Case-Based Reasoning in Design

Co-evolution as a computational and cognitive model of design