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 aims at exploiting iterative system specification as a modeling formalism including explicitly simulation mechanisms. The systems specified are dynamic with inputs/outputs (I/O) so that they can be coupled in a modular way. The iterative specification consists of a decomposition of the I/O behavior of a system into trajectory segments. We prove that specific decompositions can be combined while ensuring that the overall I/O behavior is correctly represented. While generic in nature, the approach is validated against spiking neuronal modeling applications.

Iterative Specification as a Modeling and Simulation Formalism for I/O General Systems

A central issue in modelling, the "structural inference issue," is whether it is possible to ascertain, even allowing an infinite comparison of behavioral data, that a model truly represents the mechanisms underlying a real behavior. Employing the tools of the author's Theory of Modelling and Simulation (Wiley), a heirarchy of levels of system specification is introduced; the above question then becomes one of inferring from knowledge on one level structural information about an adjacent level--i.e., inferring that some sort of structure or function preserving morphisms holds between systems on different levels. Only under restricted circumstances, wherein certain justifying conditions are met, can one infer higher-level morphisms from lower-level ones. The paper explores some of the known justifying conditions and shows how the heirarchy enables consideration of the structural inference issue in a fundamental, rigorous manner. Epistemological implications of the analysis conclude the paper.

The Hierarchy of System Specifications and the Problem of Structural Inference

/pdf/esp-scheme-a-realization-of-system-entity-structure-in-a-5g4n3kehs0.pdf

ESP-Scheme: A Realization of System Entity Structure in a LISP Environment

Publisher Summary This chapter explores a formal mathematical model that helps to show that consensus-based, egalitarian hunter-gatherers, especially those living in mosaic environments such as those of the Near East, highland Mesoamerica, or the Andes, could not have maximized resource exploitation even had that concept been part of their ideology. Many models for the origins of agriculture view it as a way of increasing energy per unit of land, often as the result of some postulated human population pressure. In the mathematical model described in the chapter, suggestions that agriculture could have arisen in some areas as a solution to the predictable problems of resource search and scheduling encountered by any group without a decision-making hierarchy can be made. Results obtained from the model suggest that the size of the region in which resources may be maximally exploitable by hunter-gatherer groups that do not possess centralized decision making is strictly limited by the individual's abilities to gather information.

A Formal Mathematical Model for the Operation of Consensus-Based Hunting–Gathering Bands

This paper describes the design and development of the DEVS/GDDM environment, a layered simulation environment that supports data dis tribution management and allows us to study space-based quantization schemes. These schemes aim to achieve effective reduction of data commu nication in distributed simulation. After a brief review of the space-based quantization scheme and an HLA-Interface environment, we discuss the design issues of the DEVS/GDDM environ ment. We analyze system performance and scalability of the space-based quantization scheme, especially with predictive and multiplex ing extensions, and empirical results for a ballis tic missiles simulation executing on the DEVS/ GDDM environment on NT networking plat forms. The results indicate the DEVS/GDDM environment is very effective and scalable due to reduced local computation demands and ex tremely favorable communication data reduction.

Bernard P. Zeigler

Papers

Iterative Specification as a Modeling and Simulation Formalism for I/O General Systems

The Hierarchy of System Specifications and the Problem of Structural Inference

ESP-Scheme: A Realization of System Entity Structure in a LISP Environment

A Formal Mathematical Model for the Operation of Consensus-Based Hunting–Gathering Bands

Design and Development of Data Distribution Management Environment