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

Model-based engineering is defined as the pragmatic utilization of model-based practices, namely, modeling, metamodeling and model transformations in various steps of engineering. In the last decade, the simulation of technical systems has leveraged graphical modeling and model-to-text transformations, but metamodeling and model transformation practices have not become widely accessible. Thereby, the benefits of employed model-driven approaches have been limited. System Entity Structures are directed labelled graphs that were proposed in the 1980s for specifying a family of system configurations for modular and hierarchical systems. The System Entity Structure and Model Base (SES/MB) framework combines the SES ontology with the classical workflow of modeling for an interactive or automatic generation of executable simulation models. After reviewing comparable approaches in software engineering, this paper discusses the application of SES/MB framework for metamodeling and model transformations for model-based engineering of simulations of technical systems.

System entity structure and model base framework in model based engineering of simulations for technical systems

This paper describes the design and development of a discrete event simulation based, dynamic, capacity analysis tool. Manufacturing capacity analysis, traditionally approached through the prescribed tools of mathematical programming, is presently devoid of a dynamic simulation framework that delivers multi-period asset evaluations based on a real-time performance estimate. A suggested framework for this class of decisions involves periodic (quarterly), deterministic, constrained evaluation models to specify resource allocation decisions. Capacity evaluation models are inherently non-stationary due to the structural modifications that occur when adding new production resources. Homogeneity is employed here in the uniform description of heterogeneous resources as general mathematical objects, or discrete event models (DEVS). Each object represents both the individual production resource's dynamic state and static parameters. Exercising these objects via simulation optimizes resources through Return on Oper...

Capacity analysis for mixed technology production: Evaluating production ramp resource modifications via distributed simulation

Automatic visual wafer inspection will increase productivity and improve product quality of integrated circuit (IC) chips on wafers. Recently, research on such inspection has been focused on problems for classification of defects on wafers to rectify wafer fabrication errors by comparing them with predefined specification standards. This paper describes the development of a knowledge-based system which classifies defects in the recti-linear type wafer images for VLSI chips. A consultation system shell called CESM (Classification Expert System Maker) has been used to develop the classification system. CESM supports integration of low level processing, feature extraction, and final decision stages.

Using the CESM Shell to Classify Wafer Defects from Visual Data

The structural knowledge of a system represented in system entity structure (SES) supports the handling of organization issue in model compositions. A pruning operation results in a reduced structure, pruned entity structure (PES) that the SES is pruned to meet design objectives. The PES is eventually synthesized into a simulation model by combining it with models in the model base. SES is implemented in XML Metadata using Java language and Sun’s Document Object Models (DOM) specification. The SESBuilder1 software supports natural language input for an SES definition and XML instance generation of its PES. The coupling information expressed in natural language with restricted syntax processed by SESBuilder is used to compose models. As a real example, the compositions of Discrete Event Specification (DEVS) generator models, representing the US Climate Normals, are presented. The example presents the natural language input and XML instances for PES including coupling information in the SESBuilder.

Bernard P. Zeigler

Papers

System entity structure and model base framework in model based engineering of simulations for technical systems

Capacity analysis for mixed technology production: Evaluating production ramp resource modifications via distributed simulation

Using the CESM Shell to Classify Wafer Defects from Visual Data

DEVS model composition by system entity structure

On the feedback complexity of automata