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

We review Discrete-Event system Specification (DEVS) in the context of Model-based Systems Engineering (MBSE) and discuss an application of DEVS methodology to MBSE. We outline support for an envisioned MBSE development cycle of DEVS top-to-bottom MBSE capability and offer an example of mapping UML activity diagrams into executable activity-based DEVS models. We close with conclusions and future research directions.

DEVS and MBSE: A review

A new criterion for series-parallel irreducibility is given which makes no reference to underlying semigroups but involves only series-parallel connection operations.

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A note on series parallel irreducibility

Simulation and Model-Based Methodologies: An Integrative View.@@@UKSC '84: Proceedings of the 1984 UKSC Conference on Computer Simulation.

The ability to manage and exploit geographically distributed systems of service providers is rather limited in today engineering solutions. Existing techniques suffer from three main problems: first, current techniques cannot provide brokering in managing loosely coupled service providers. Second, the engineering design of existing management tools does not provide enough expressive capabilities for varying user behaviors or when different domains are encountered. Third, lack of interaction between different requestors and providers yields inefficient and very costly agreements. In this thesis, we will present an automated Domain-Independent Marketplace architecture that allows user agents to interact with provider agents using two simple and yet powerful negotiation protocols which define the rules of interactions in multi-agent environments. Having a trusted third party marketplace supports privacy and transparency among collaborative agents and service providers. Service providers have different capabilities depending on the domain of interest. Such providers can be radar sensors as in oceanography surveillance systems, print servers in distributed printing jobs community, or they can be online stores providing products on the web in the E-commerce domain. In order to provide negotiation in different domains, a dynamic message structuring capability is needed. A key role to support such an expressive power is to design an ontology that contains specialization relations between the different domains of interest. The automation of integrating the Domain-Dependent message structure Ontology with the Domain-Independent marketplace architecture gives the designer a powerful tool in which systems can be tailored based on the operational purposes and objectives.
The System Entity Structure (SES) methodology, which is a formalism to define hierarchical relations among entities, is used to build the required message structures Ontology automatically through the creation of SES natural language. The architecture design of the Marketplace suggests different phases and functionalities which are mapped and implemented using the Discrete Event System Specifications (DEVS). DEVS/Service Oriented Architecture (DEVS/SOA) is used to validate our system and show a proof of the concept by deploying models of printing jobs in a web-services multi-server environment for printing server domain.

Bernard P. Zeigler

Papers

DEVS and MBSE: A review

A note on series parallel irreducibility

Simulation and Model-Based Methodologies: An Integrative View.@@@UKSC '84: Proceedings of the 1984 UKSC Conference on Computer Simulation.

An automated methodology for negotiation behaviors in multi-agent engineering applications

Using DEVS for Full Life Cycle Model-Based System Engineering in Complex Network Design