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 describes a method for creating synthetic air tracks without the aide of a sophisticated simulator. The proposed method creates a position and velocity function for an aircraft given its starting location and speed, altitude, and heading on each leg of its height. An optimization procedure is used to fit the position and velocity functions to the supplied data. Several examples of tracks produced by the method are shown in the paper. The synthetic tracks that are produced by this method can be used to facilitate testing of command and control systems and to enhance any simulation that requires air tracks that satisfy specific constraints on time, speed, and position.

A Method for Generating Synthetic Air Tracks

From 18.01.04 to 23.01.04, the Dagstuhl Seminar 04041 ``Component-Based Modeling and Simulation'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.

04041 Abstracts Collection - Component-Based Modeling and Simulation.

This chapter discusses the discrete event system specification (DEVS) formalism and its implementation in DEVS-Scheme. Its basic features help to understand how hierarchical, modular simulation models are specified in DEVS-Scheme. The chapter describes that DEVS formalism provides a means of specifying a mathematical object called a system. Basically, a system has a time base, inputs, states, and outputs, and functions for determining next states and outputs given current states and inputs. The insight provided by the DEVS formalism is that it characterizes the way discrete event simulation languages specify discrete event system parameters. It is more than just a means of constructing simulation models. It provides a formal representation of discrete event systems capable of mathematical manipulation just as differential equations serve this role for continuous systems. Such manipulation includes behavioral analysis, whereby properties of the behavior of a system are deduced by examining its structure. In the DEVS-Scheme realization, the DEVS formalism is refined so that both the input and output sets X, Y consist of pairs of the form (port, value). Thus, an external input event of the form x = ( p, v ) signals the fact that a value v has been received at an input port p . Similarly, y = ( p, v ) represents the sending of a value v to output port p.

Devs formalism and devs-scheme

We propose to extend the framework for relating information and energy via activity [1]. to the design of hardware. Pifer et. al. [2] present an approach to DEVS-based Hardware Design, Synthesis, and Power Optimization that exploits activity concepts. Here we summarize this approach with emphasis on the role of activity. We also provide some results from [2] that show the utility of various mechanisms to reduce power consumption based on DEV Sand/or activity features.

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Linking Activity to Information and Energy in Hardware

Discrete Event Modeling and Simulation (M&S) and Machine Learning (ML) are two frameworks suited for system modeling which when combined can give powerful tools for system optimization for example. This paper details how discrete event M&S could be integrated into ML concepts and tools in order to improve the design and use of ML frameworks. An overview of different improvements are given and three concerning Reinforcement Learning (RL) are implemented in the framework of the DEVS formalism.

Bernard P. Zeigler

Papers

A Method for Generating Synthetic Air Tracks

04041 Abstracts Collection - Component-Based Modeling and Simulation.

Devs formalism and devs-scheme

Linking Activity to Information and Energy in Hardware

Discrete Event Modeling and Simulation Aspects to Improve Machine Learning Systems