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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

How to Do Things With Words

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

An Introduction to MultiAgent Systems.

This paper gives an introduction to the new research area of Organic Computing and shows chances, opportunities and problems currently tackled by researchers. First the visions that lead to this new research area are discussed briefly. It is shown that the notion of emergence, a central phenomenon in Organic Compting, is a typical bottom-up effect with the interesting property of generating order from randomness. The classical design, however, is a top-down process. This apparent contradiction can be overcome by introducing so-called Observer/Controller architectures leading to the possibility to controlled emergence. The paper concludes with a description of current research problems in Organic Computing.

Organic computing: on the feasibility of controlled emergence

Technical scenarios in areas like automotive or production systems will increasingly consist of a large number of components cooperating in potentially unlimited and dynamically changing networks to satisfy the functional requirements of their execution environment. Due to the high complexity it will be impossible to explicitly design the behaviour of the components for every potentially arising situation. Therefore, it will be necessary to leave an adequate degree of freedom allowing for a self-organised behaviour. Organic Computing (OC) has developed the vision of selforganising systems adapting robustly to dynamically changing environments without running out of control. This paper focuses on the design of a generic system architecture which allows for self-organisation but at the same time enables adequate reactions to control the – sometimes completely unexpected – emerging global behaviour of these self-organised technical systems.

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Towards a generic observer/controller architecture for Organic Computing.

Organic Computing (OC) and other research initiatives like Autonomic Computing or Proactive Computing have developed the vision of systems possessing life-like properties: they self-organize, adapt to their dynamically changing environments, and establish other so-called self-x properties, like self-healing, self-configuration, self-optimization, etc. What we are searching for in OC are methodologies and concepts for systems that allow to cope with increasingly complex networked application systems by introduction of self-x properties and at the same time guarantee a trustworthy and adaptive response to externally provided system objectives and control actions. Therefore, in OC, we talk about controlled self-organization.Although the terms self-organization and adaptivity have been discussed for years, we miss a clear definition of self-organization in most publications, which have a technically motivated background.In this article, we briefly summarize the state of the art and suggest a characterization of (controlled) self-organization and adaptivity that is motivated by the main objectives of the OC initiative. We present a system classification of robust, adaptable, and adaptive systems and define a degree of autonomy to be able to quantify how autonomously a system is working. The degree of autonomy distinguishes and measures external control that is exerted directly by the user (no autonomy) from internal control of a system which might be fully controlled by an observer/controller architecture that is part of the system (full autonomy). The quantitative degree of autonomy provides the basis for characterizing the notion of controlled self-organization. Furthermore, we discuss several alternatives for the design of organic systems.

Adaptivity and self-organization in organic computing systems

The apparatus for identity verification using a data card contains at least one terminal and a security service station. The terminal(s) and the station are connected to each other via a communication system. The terminal is provided with a central processing unit including a memory, a card reader for reading data from the data card, a sensor or number input device for introducing personal identification information, and a crypto module. The crypto module encrypts and decrypts data received from the memory under the control of the central processing unit. The security service station likewise also contains a central processing unit including a memory, and a crypto module. This station also contains a comparator for comparing personal identification information with reference personal identification information. Both kinds of information are transmitted to the station from the terminal.

Apparatus and method for cryptographic identity verification

In the past, the focus of the computer industry has been to improve hardware performance and add more and more features to the software. As a result, more and more appliances surrounding us are equipped with embedded computational power and wireless communication. As such, they become ever more flexible and multifunctional, and almost indispensable in daily life. On the other hand, the resulting systems become increasingly complex and unreliable, posing new challenges to designer and user. Organic Computing (OC) has the vision to address the challenges of complex distributed systems by making them more life-like (organic), i.e. endowing them with abilities such as self- organization, self-configuration, self-repair, or adaptation. The designer's task is simplified, because it is no longer necessary to exactly specify the low-level system behavior in all possible situations that might occur, but instead leaving the system with a certain degree of freedom which allows it to react in an intelligent way to new situations. Also, use of such systems is simplified, as they can be controlled by setting few high-level goals, rather than having to manipulate many low-level parameters with unclear influence. In this paper, we give a general introduction to OC, and propose a generic observer-controller architecture as a framework for designing OC systems. Then, it is shown how to use this architecture at the example of a traffic light controller. The paper concludes with a summary and a discussion of future challenges.

Christian Müller-Schloer

Papers

Organic computing: on the feasibility of controlled emergence

Towards a generic observer/controller architecture for Organic Computing.

Adaptivity and self-organization in organic computing systems

Apparatus and method for cryptographic identity verification

Organic Computing Addressing Complexity by Controlled Self-Organization