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

In the context of a DARPA ASTT project, we are developing an HLA-compliant distributed simulation environment based on the DEVS formalism. This environment will provide a user- friendly, high-level tool-set for developing interoperable discrete and continuous simulation models. One application is the study of contract-based predictive filtering. This paper presents a new approach to predictive filtering based on a process called 'quantization' to reduce state update transmission. Quantization, which generates state updates only at quantum level crossings, abstracts a sender model into a DEVS representation. This affords an alternative, efficient approach to embedding continuous models within distributed discrete event simulations. Applications of quantization to message traffic reduction are discussed. The theory has been validated by DEVSJAVA simulations of test cases. It will be subject to further test in actual distributed simulations using the DEVS/HLA modeling and simulation environment.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Theory of quantized systems: formal basis for DEVS/HLA distributed simulation environment

Discrete Event Specification (DEVS) environments are implemented over middleware systems such as HLA, RMI, CORBA and others. DEVS exhibits concepts of systems theory and modeling and supports capturing the system behavior from the physical and behavioral perspectives. Further, they are implemented using object-oriented languages like Java and C++. This research work uses the Java platform to implement DEVS over a Service Oriented Architecture (SOA) framework. Called the DEVS/SOA, the framework supports a development and testing environment known as DEVS Unified Process that is built on a model continuity-based lifecycle methodology. DEVS Unified Process allows DEVS-based Modeling and Simulation (M&S) over net-centric platforms using DEVS/SOA. This framework also provides the crucial feature of run-time composability of coupled systems using SOA. We describe the architecture and designs of the server and the client. The client application communicates with multiple servers hosting DEVS simulation services. These simulation services are developed using the proposed symmetrical services architecture wherein the server can act as both a service provider and a service consumer contrary to the unidirectional clientâserver paradigm. We also discuss how this services-based architecture provides solutions for cross-platform distributed M&S. We demonstrate the DEVS/SOA framework with a scenario of Joint Close Air Support specified in Business Process Modeling Notation (BPMN). We also provide a real-world application of network health monitoring using DEVS/SOA-layered architectural framework.

DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process

Model continuity refers to the ability to transition as much as possible a model specification through the stages of a development process. In this paper, the authors show how a modeling and simulation environment, based on the discrete event system specification formalism, can support model continuity in the design of dynamic distributed real-time systems. In designing such systems, the authors restrict such continuity to the models that implement the system's real-time control and dynamic reconfiguration. The proposed methodology supports systematic modeling of dynamic systems and adopts simulation-based tests for distributed real-time software. Model continuity is emphasized during the entire process of software development $the control models of a dynamic distributed real-time system can be designed, analyzed, and tested by simulation methods, and then smoothly transitioned from simulation to distributed execution. A dynamic team formation distributed robotic system is presented as an example to show how model continuity methodology effectively manages the complexity of developing and testing the control software for this system.

Model continuity in the design of dynamic distributed real-time systems

The paper proposes a layered architectural framework to support agent based system development in a collaborative, multidisciplinary engineering setting. This architecture is viewed from two distinct perspectives. First, the environment must enable agent based modeling and simulation. Second, it should support concurrent (team oriented) engineering. The main focus is on the proposed layered architecture delineating various needs of an agent based system, thus supporting incremental specification design, implementation, and testing. In our discussions, we distinguish between performing agents and simulated agents. The former refers to agents as they are performing their tasks in real-world settings. The latter refers to agents that have their behavior simulated in a virtual environment. In these terms, the proposed framework is intended to form the basis for environments that support development of agents, in both performance and simulation modes, as well as in hybrid combination (both performing and simulated agents interacting at the same time). The proposed framework is a step toward realization of agent based systems under the umbrella of the simulation based acquisition (SBA) initiative of the US Department of Defense.

Bernard P. Zeigler

Papers

Theory of quantized systems: formal basis for DEVS/HLA distributed simulation environment

DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process

Model continuity in the design of dynamic distributed real-time systems

A layered modeling and simulation architecture for agent-based system development

Methodology in Systems Modelling and Simulation