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

This paper studies the control of a class of discrete event processes, i.e. processes that are discrete, asynchronous and possibly nondeter-ministic. The controlled process is described as the generator of a formal language, while the controller, or supervisor, is constructed from the grammar of a specified target language that incorporates the desired closed-loop system behavior. The existence problem for a supervisor is reduced to finding the largest controllable language contained in a given legal language. Two examples are provided.

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Supervisory control of a class of discrete event processes

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

https://www.ufz.de/export/data/2/100065_GrimmODD.pdf

A standard protocol for describing individual-based and agent-based models

This paper explores the Information Exchange Framework (IEF) concept of distributed data fusion sensor networks in Network-centric environment. It is used to build up integrative battlefield pictures through the Battle Management Language (BML) and System Entity Structure (SES) ontology for C2 systems. The C2 process requires multi-level information to assess the current situation in a sound manner. Superiority of information is critical factor to win battles. The SES is an ontology framework that can facilitate information exchange in a network environment. From the perspective of the SES framework, BML serves to express pragmatic frames, since it can specify the information desired by a consumer in an unambiguous way. We explain the idea of information exchange in the SES ontology via BML and demonstrate pruning and transformation processes of SES with proof-of-concept examples. Keywords-component; SES; Ontology; Information Exchange Framework; BML; Pragmatic Frame; Data Fusion

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BML Enabled Information Exchange Framework in SES Ontology for C2

The next century will be characterized by ambitious attempts to design, construct, or manage ultralarge systems such as high bandwidth global communication networks, flexible manufacturing systems with high autonomy, and ecosystems distributed over large geographical regions Systems concepts and principles are needed to deal with such overwhelming complexity Fads continue to flash on the scene and fade just as fast The enduring advances have a fundamental robustness consistent with systems-based methodology

Fundamental Systems Concepts: "The Right Stuff" for 21st Century Technology

: A simple bacterial cell (Escherichia coli) has been modeled, and the input-output behavior of the model has been simulated as a program in FORTRAN IV for an IBM 360/67 digital computer. Automata theoretic analysis of the homomorphic model underlying the computer simulation enables us to investigate the information content and complexity of the simulation and of the measurement space representing our data base. The simulated cell is able to adjust its enzymes and DNA to grow in different chemical environments using allosteric modification of enzymes, and repression of RNA synthesis. It grows at realistic rates and achieves limited metabolic stability. (Author)

Computer simulation of a living cell: multilevel control systems.

Representation mechanisms for discrete event simulation models are intended to facilitate the important conceptualization and communication components of model building science. Moreover, effective design of future computer-based decision support systems will rely upon well founded principles of model management. Modular and hierarchical concepts facilitate model building. This article extends the theory of discrete event system specification to provide a rigorous basis for modular and hierarchical simulation model construction. Practical applications of the theory are discussed and include programming constructs for modular and hierarchical simulation, inter-translation between modular and non-modular model description, and implementation of modular separation between model and experimental frame components of simulation programs.

Theory of discrete event specified models: modularity, hierarchy, experimental frames

System theoretic foundations for emergent behavior modeling: the case of emergence of human language in a resource-constrained complex intelligent dynamical system

Bernard P. Zeigler

Papers

BML Enabled Information Exchange Framework in SES Ontology for C2

Fundamental Systems Concepts: "The Right Stuff" for 21st Century Technology

Computer simulation of a living cell: multilevel control systems.

Theory of discrete event specified models: modularity, hierarchy, experimental frames

System theoretic foundations for emergent behavior modeling: the case of emergence of human language in a resource-constrained complex intelligent dynamical system