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

The recent development in cloud computing and Web service delivery encouraged companies from different disciplines to offer their services to customers over the Internet. Since different domains use different language of encounter to establish their contracts, a generic negotiation framework that can be tailored to a specific domain is needed. In this paper, we will present a generic domain-independent marketplace architecture that allows user agents to interact with service providers using two simple and yet powerful negotiation protocols. Service providers have different capabilities depending on the domain of interest. Hence, a dynamic message structuring capability is needed. A key solution to support expressive power is to design an ontology that contains specializations between different domains. The Integration of the domain-dependent ontology with the domain-independent marketplace gives the designer a powerful tool in which systems can be tailored based on the operational purpose. We demonstrate a proof of the concept by implementing the negotiation framework using Discrete Event Specification/Service Oriented Architecture (DEVS/SOA) environment.

Ontology-based marketplace for supporting negotiation in different scientific applications

Conditions under which compositions of component systems form a well-defined systemof-systems here are formulated at a fundamental level. Statement of what defines a well -defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is nondeterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

/pdf/emergence-at-the-fundamental-systems-level-existence-guni2mf2g7.pdf

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

Model Development and Execution Process with Repositories, Validation, and Verification In this chapter, we discuss another modeling and simulation environment that supports both development and storage of families of DEVS models for Systems of Systems. Component-based System Modeler (CoSMo) is grounded in a unified logical, persistence, and visual model development concept. We show how you can develop; store, retrieve, and instantiate DEVS SoS models such as those for service oriented and cloud systems (Chaps. 14 and 15). We develop a unified concept supporting logical, visual, and persistence modeling and simulation framework (CoSMoS) which lends itself for data (XML Schema) modeling and XML Schema code as well as cellular automata modeling. We show how the concept of SW/HW co-design for systems of systems fits seamlessly into the CoSMoS framework. While being based on the same underlying concepts of DEVS model construction, MS4 Me™ and CoSMoS offer somewhat different perspectives on support for SoS model construction.

Model Development and Execution Process with Repositories, Validation, and Verification

System Entity Structures (SES) are used to define families of systems. In this context they are employed in combination with a model base (MB) to describe a set of simulation models. Using a framework, simulation models are generated from the SES/MB in a goal-oriented manner, executed and their results analyzed. The entire process is automated, iterative and reactive. A concrete system variant in the family of systems is derived by pruning an SES. For the process of automated model generation, it is necessary to provide automatic pruning mechanisms. Especially for some types of nodes comprised in an SES, automatic pruning is a big challenge. Here, the challenges that arise when pruning SES containing hierarchies of multi-aspect and specialization nodes are analyzed and a solution is proposed.

/pdf/automated-reactive-pruning-of-system-entity-structures-for-5ebx3zsee2.pdf

Bernard P. Zeigler

Papers

Ontology-based marketplace for supporting negotiation in different scientific applications

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

Model Development and Execution Process with Repositories, Validation, and Verification

Automated, Reactive Pruning of System Entity Structures for Simulation Engineering

Knowledge-Based Simulation Environment Techniques A Manufacturing System Example