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.

/pdf/supervisory-control-of-a-class-of-discrete-event-processes-45fsmhdhbo.pdf

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

Sequential machines can be realized by series and parallel connections of components. These connections can be automatically learned by simulation, rating and selecting component models. Our goal here is threefold: (i) discuss implications for model reconstruction and reconfiguration in model engineering, (ii) present mathematical analyses of search simulation results, and (iii) conjecture that the whole search space of achievement components seems to be well ordered. This conjecture opens new perspectives for studying parallel and series connections of components upon a simulation-based framework. More generally, the automatic learning demonstrated here constitutes a formal basis for model reconstruction and reconfiguration in model engineering.

https://hal.archives-ouvertes.fr/hal-01316922/document

A conjecture from learning simulations of series and parallel connections of components

Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

Modelling and Simulation Methodology in the Artificial Intelligence Era: A Selection of Papers from the Working Conference, Wageningen, the Netherlands, July 1-5, 1985

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

With the increased demand for distributed simulation to support large-scale modeling and simulation applications, much research has focused on developing a suitable framework to support simulation across a heterogeneous computing network. Middleware based solutions have dominated this area for years, however, they lack the flexibility for model partitions and dynamic repartition due to their innate static natures. In this dissertation, a novel approach for DEVS based distributed simulation framework is proposed and implemented. The objective of such a framework is to distribute simulation entities across network nodes seamlessly without any of the commonly used middleware, as well as to support adaptive and reconfigurable simulations during runtime. This new approach, called DEVS/RMI, is proved to be well suited for complex, computationally intensive simulation applications and its flexibility in a distributed computing environment promotes a rapid development of distributed simulation applications. A hilly terrain continuous spatial model is studied to show how DEVS/RMI can easily refactor the simulations to accommodate both increases of the resolution and computation nodes. Furthermore, an agent-based valley fever model is investigated in this dissertation with particular interests on the concept of DEVS "activity". Dynamic reconfiguration of distributed simulation is then exemplified using the "activity" based model repartition in a DEVS/RMI supported environment. The flexibility and reconfigurable nature of DEVS/RMI open up further investigations into the relationship between speedup of a simulation and the partition or repartition algorithm used in a distributed simulation environment.

/pdf/toward-a-flexible-and-reconfigurable-distributed-simulation-4nubuf55s6.pdf

Bernard P. Zeigler

Papers

A conjecture from learning simulations of series and parallel connections of components

Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

Modelling and Simulation Methodology in the Artificial Intelligence Era: A Selection of Papers from the Working Conference, Wageningen, the Netherlands, July 1-5, 1985

04041 Abstracts Collection -- Component-Based Modeling and Simulation

Toward a flexible and reconfigurable distributed simulation: a new approach to distributed devs