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

Since the early 70s, the M&S community has been trying to formulate approaches to modeling as system specification formalisms. As seen in the previous chapters of this book, the proliferation of DEVS-based M&S engines has brought the need to improve and standardize DEVS tools, facilitating the work of DEVS designers independently of the programming language implementations or algorithmic code expressions used. To understand the problem, let us consider that DEVS categorically separates the Model, the Simulator, and the Experimental frame. Building on this separation of concerns, the DEVS Protocol specifies the abstract simulation engine that correctly simulates DEVS atomic and coupled models. Interpreted in a distributed simulation context, the DEVS abstract simulator gives rise to a general protocol that has specific mechanisms for declaring who takes part in the simulation (the federates). It also specifies how federates interact in an iterative cycle that controls how time advances, when federates exchange messages, and perform internal state updating. A significant feature in comparison to simulation based on the HLA standard, is that if the federates in simulation are DEVS compliant then the simulation can be proved to be correct in the sense that the DEVS closure under coupling theorem guarantees a well-defined resulting structure and behavior.

An Introduction to DEVS Standardization

Complex spatially-extended systems consist of numerous sub-systems leading to large simulation execution times. One approach to reducing these execution times is designing a simulation engine to allocate its attention to subsystems in proportion to their activity levels. In this paper, we consider a large scale simulation of a physics-based fire spread model. This model is discretized using a recently developed numerical method called quantization and implemented using discrete event simulation. In this paper, we provide comparisons between the quantization method and usual Euler discrete-time methods. The aim is to demonstrate the ability of quantization and discrete event simulation to focus on active sub-systems, thus significantly reducing execution time for large heterogeneous systems.

/pdf/discrete-event-simulation-of-large-scale-spatial-continuous-4cw6fmgjmm.pdf

Discrete event simulation of large-scale spatial continuous systems

Glossary Behavior The observable manifestation of an interaction with a system DEVS Discrete Event System Specification formalism describes models developed for simulation; applications include simulation based testing of collaborative services Endomorphic Agents Agents that contain models of themselves and/or of other endomorphic Agents. Levels of Interoperability Levels at which systems can interoperate such as syntactic, semantic and pragmatic. The higher the level, the more effective is information exchange among participants.

Artificial Intelligence in Modeling and Simulation.

Conventional modeling theory gives support only for representing model behavior, providing little aid for describing changes in model structure. Some models are better represented by changes in their structure. Instead of forcing this changes to be represented at the simple behavioral level, a strong theoretical support is needed to allow the representation of structural changes in a natural way. We present a modeling methodology for representing variable structure systems. Examples of such systems include adaptive computer architectures, ecological systems, fault tolerating computers. We describe an application of this methodology to the modeling and simulation of an adaptive computer architecture. >

Variable DEVS-variable structure modeling formalism: an adaptive computer architecture application

An approach to model complexity is outlined which is based upon a hierarchy of system specifications and morphisms, and related complexity measures. The measures are justified in terms of the resources required by a computational process which generates model behavior given model structure. We study whether the complexity tends to decrease or not, when a lumped model is related to its original base model by a morphism.

Bernard P. Zeigler

Papers

An Introduction to DEVS Standardization

Discrete event simulation of large-scale spatial continuous systems

Artificial Intelligence in Modeling and Simulation.

Variable DEVS-variable structure modeling formalism: an adaptive computer architecture application

Simulation based structural complexity of models