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

A multi-scale spatial ecological model of a wet sclerophyllous forest subject to recurrent fires is presented. The model is specified in a Discrete Event Systems framework (DEVS) (Zeigler, 1990) interfaced with a Geographic Information System (GIS), and includes the ability to simulate landscape dynamics at several levels of resolution simultaneously. This is achieved by encoding a modular hierarchical representation of the forest landscape components into a set of nested, interconnected, and spatially referenced dynamic models. The results of the landscape dynamics simulations are displayed as sequences of maps through time, illustrating the potential of this modeling methodology for dealing with complex hierarchical structures that operate at several spatial and temporal resolutions.

Modeling multi-scale spatial ecological processes under the discrete event systems paradigm

This tutorial will emphasize concepts and methodology and will relate them to languages and software environments which are becoming available to support these concepts. We will show how high level specification of discrete event models with hierarchical and modular properties is crucial to the sound integration of knowledge representation approaches of artificial intelligence. We will discuss examples of hierarchical modular models exhibiting self-modifying structure capabilities and show how they may be implemented in conventional, as well as object-oriented symbolic languages. Finally, structuring of model bases for simulation environments will be presented and example tools illustrated.Specifically, we shall discuss the following topics:High level specification of discrete event models with hierarchical and modular properties using pseudo-code formalism.Implementation of such specifications in procedural languages: How to express such pseudo-code in conventional languages such as SIMSCRIPT II.5 as well as in LISP-based languages.Examples of such hierarchical modular models exhibiting self-modifying structure capabilities and embedded artificial intelligence.Model/Knowledge Base Design: Organizing models using the system entity structure to constitute a reusable knowledge base.Background for this tutorial may be found in (Zeigler 1984, 1985).

/pdf/hierarchical-modular-modeling-knowledge-representation-2bl9j5fl5y.pdf

Hierarchical modular modeling/knowledge representation

This paper describes our effort to implement DEVS on a TINI Chip which has limited memory and processing ability. A set of well-defined DEVS interfaces make it possible to define a just-as-needed DEVS real time environment and run on the chip efficiently. As a case study for real time data gathering, processing and management, a DEVS coupled model has been composed from a set of primitive DEVS atomic models in this application area. This coupled model gets real time temperature from a sensor and runs data processing such as quantizer, moving averager, maximum and minimum extractor, etc. before it transfers the data to database.

DEVS-on-a-chip: implementing DEVS in real-time Java on a tiny Internet interface for scalable factory automation

“Emergence” is a descriptive term that is gaining currency for a set of phenomena that are becoming increasingly important to understand scientifically. Recently, several works have used the context of socio-technical systems of systems to serve as a germane abstraction for characterizing emergence. In this note, we formulate conditions as a tri-layer architecture required for promoting the emergence of desired behaviors in a system of systems and managing unintended undesired effects.

Bernard P. Zeigler

Papers

Modeling multi-scale spatial ecological processes under the discrete event systems paradigm

Hierarchical modular modeling/knowledge representation

DEVS-on-a-chip: implementing DEVS in real-time Java on a tiny Internet interface for scalable factory automation

A note on promoting positive emergence and managing negative emergence in systems of systems

A Framework for Multiresolution Optimization in a Parallel/Distributed Environment