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

The present impact of general systems orientation, i.e., theory and approach, on the simulation community is briefly outlined and assessed. Prospects for the future are contingent upon the evolution of educational and corporate settings conducive to “modelling in the large” methodologies.

Impact of general systems orientation: Present and future

Despite the increasing use of software system development methodologies that follow a highly-structured and systematic process, there remain critical limitations that need more advanced treatment. One such limitation is the failure to explicitly incorporate modeling and simulation (M&S) methodology, which becomes essential as the scale of systems under development exceeds the range supported by analytical tools. Other limitations include the lack of automation to reduce routine work and incoherence among the abstractions developed at various stages in development. Originally introduced as formalism for discrete event M&S, the Discrete Event System Specification (DEVS) methodology has become an engine for advances within the wider area of information technology. In this paper, we present a new DEVS-based tool called the ‘AutoDEVS’ that automates systems development and exploits ‘model continuity’ to maintain coherence through the development process and to support test artifact continuity and traceability ...

AutoDEVS: A Methodology for Automating Modeling and Simulation Software Development and Testing of Interoperable Systems

An approach to operating system modelling is described in which the simultaneous and synergistic application of tractable queueing theory models and their simulatable refinements is essential. Cross checking of these models against each other and against the real system results in a degree of confirmation not achievable by relying on either analytic or simulation models alone.

Formalization and confirmation of the Boyd–Epley operating system model

Towards a formal theory of modelling and simulation : II

: A critical aspect and differentiator of a System of Systems (SoS) versus a single monolithic system is interoperability among the constituent disparate systems. A major application of Modeling and Simulation (M&S) to SoS Engineering is to facilitate system integration in a manner that helps to cope with such interoperability problems. A case in point is the integration infrastructure offered by the DoD Global Information Grid (GIG) and its Service Oriented Architecture (SOA). In this paper, we discuss a process called DEVS Unified Process (DUNIP) that uses the Discrete Event System Specification (DEVS) formalism as a basis for integrated system engineering and testing called the Bifurcated Model-Continuity lifecycle development methodology. DUNIP uses an XML-based DEVS Modeling Language (DEVSML) framework that provides the capability to compose models that may be expressed in a variety of DEVS implementation languages. The models are deployable for remote and distributed real-time executing agents over the Service Oriented Architecture (SOA) middleware. We also compare DUNIP with the Model Driven Architecture (MDA) paradigm and provide overview of various projects that led to the formulation of DUNIP.

http://saurabh-mittal.com/downloads/Mittal_Zeigler_DEVS_Unified_Process.pdf

Bernard P. Zeigler

Papers

Impact of general systems orientation: Present and future

AutoDEVS: A Methodology for Automating Modeling and Simulation Software Development and Testing of Interoperable Systems

Formalization and confirmation of the Boyd–Epley operating system model

Towards a formal theory of modelling and simulation : II

DEVS Unified Process for Web-Centric Development and Testing of System of Systems