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 deals with PDEVS (Parallel Discrete Event System specification) protocol performance prediction using activity patterns with Finite Probabilistic DEVS. In 2015, Zeigler et. al showed that the PDEVS protocol is a simple-to-implement distributed simulation protocol that can provide speedups to match the best performance of classical algorithms for most situations. We propose to model the PDEVS protocol using a Markov Continuous Time Model (CTM) which is set up using two parameters: (i) the probability of a state giving an output according to input patterns; (ii) the rate of the coordinator's release of imminents. These parameters are computed using Activity metrics and inserted into the PDEVS modeling scheme in order to predict the performance of a PDEVS protocol in the framework of distributed simulations. The approach is supported by the DEVSimPy and Ms4Me environments and is highlighted by a pedagogical example.

PDEVS protocol performance prediction using activity patterns with finite probabilistic devs

Automating DEVS over data distribution service for high performance and interoperability.

This paper elaborates an integrated hierarchy of model description formalisms previously proposed. We discuss the advantages of the hierarchical approach vis a vis a standardized universal model description language. Among the principal advantages are the opportunities provided for concise model specification while at the same time enabling equivalence testing of models expressed in diverse formalisms.

Systems hierarchy as a basis for simulation model description

In investigating front end model development, an environment is described that allows for model construction through pruning a domain specific system entity structure. The preformal stages of the model will be represented by a verb phrase. This representation is sufficiently derailed to serve as the basis for model construction and yet sufficiently "soft" to support knowledge acquisition during model construction. This paper establishes the adequacy of this representation. >

Bernard P. Zeigler

Papers

PDEVS protocol performance prediction using activity patterns with finite probabilistic devs

Automating DEVS over data distribution service for high performance and interoperability.

Systems hierarchy as a basis for simulation model description

Verb phrase model specification via system entity structures

Abstraction: Constructing Model Families