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

: Modeling large scale systems with natural and artificial components requires storage of voluminous amounts of knowledge/information as well as computing speed for simulations to provide reliable answers in reasonable time. Computing technology is becoming powerful enough to support such high performance modeling and simulation. This report proposes a high performance simulation based optimization environment to support the design and modeling of large scale systems with high levels of resolution, and represents the results of contract F3O602-95-C-O25O, "Methodology for Simulation Model Abstraction."

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA339058&Location=U2&doc=GetTRDoc.pdf

High Performance Optimization and Abstraction of Large Simulation Models

: This effort investigated programming languages computer processors and simulation techniques capable of supporting the development of an advanced framework for human performance process model research The framework, the holon cognitive architecture, conceptualizes the mind as consisting of a set of interactive agents These agents are abstract models of neuronal activity occuring in the brain This document contains an analysis of the computation requirements needed to simulate human performance process models based upon the holon cognitive architecture and suggest a growth path based upon a heterogeneous computing environment (KAR) P 3

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Alternative Analysis for Computational Holon Architectures.

Implementing the DEVS formalism on a parallel SIMD architecture

Publisher Summary
This chapter discusses the object-oriented programming as the discrete event system specification (DEVS) scheme environment is built upon this paradigm. It also discusses the concepts of modular, hierarchical models, and the system entity structure. Object-oriented programming is a paradigm in which a software system is decomposed into subsystems based on objects. Computation is done by objects exchanging messages among themselves. The paradigm enhances software maintainability, extensibility, and reusability. Conventional software systems tend to consist of collections of subprograms based on functional decomposition techniques that concentrate on algorithmic abstractions. The chapter discusses that object-oriented programming encourages a much more decentralized style of decision-making by creating objects whose existence may continue throughout the life of the program. These objects act as experts in their own task assignments by providing them with the appropriate knowledge. This distribution and localization of knowledge simplifies the main routine and relieves it of much of its decision-making burden. While object-oriented programming provides the means for implementing knowledge-based simulation environments, the system entity structure/model base (SES/MB) framework provides the ends to which these means are applied.

Bernard P. Zeigler

Papers

High Performance Optimization and Abstraction of Large Simulation Models

Alternative Analysis for Computational Holon Architectures.

Implementing the DEVS formalism on a parallel SIMD architecture

Chapter 2 – BASICS

Canonical realization of general time systems