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

System entity structure (SES) is a structural knowledge representation scheme that contains knowledge of decomposition, taxonomy, and coupling of a system. Formally, the SES is a labeled tree with attached variable types that satisfy certain axioms. Described is a realization of the SES in Scheme (a Lisp dialect), that is called ESP-Scheme. Specifically, the computer representation of SES and main operations on SES are presented, and then facilities provided by ESP-Scheme are described. Two examples of application are discussed: a parallel processor model and a simulation study of a university phone registration system. ESP-Scheme acts as a model base management system in DEVS-Scheme, a knowledge-based simulation environment. It supports specification of the structure of a family of models, pruning the structure to a reduced version, and transforming the latter to a simulation model by synthesizing component models in the model base. >

System entity structuring and model base management

Section 1: Conceptual Bases for System Modelling and Design.- 1: Model-Based Activities: A Paradigm Shift.- 2: System Paradigms as Reality Mappings.- 3: General Systems Framework for Inductive Modelling.- 4: System Theoretic Foundations of Modelling and Simulation.- 5: The Tricotyledon Theory of System Design.- 6: Concepts for Model-Based Policy Construction.- Section 2: Model-Based Simulation Architecture.- 7: Structures for Model-Based Simulation Systems.- 8: Symbolic Manipulation of System Models.- 9: Concepts for an Advanced Parallel Simulation Architecture.- Section 3: Impact of Formalisms on Model Specification.- 10: GEST-A Modelling and Simulation Language Based on System Theoretic Concepts.- 11: Continuous and Discontinuous-Change Models: Concepts for Simulation Languages.- 12: Discrete Event Formalism and Simulation Model Development.- Section 4: Model Identification, Reconstruction, and Optimization.- 13: Structure Characterization for I11-Defined Systems.- 14: Reconstructability Analysis: An Overview.- 15: SAPS-A Software System for Inductive Modelling.- 16: Optimization in Simulation Studies.- Section 5: Quality Assurance in Model-Based Activities.- 17: Quality Assurance in Modelling and Simulation: A Taxonomy.- 18: How to Enhance the Robustness of Simulation Software.- 19: Simulation Model Validation.- 20: Critical Issues in Evaluating Socio-Economic Models.- Section 6: Contributed Workshop Presentations.- Group 1: Model-Based Simulation Architecture.- Group 2: Impact of Formalisms on Model Specification.- Group 3: Model Identification, Reconstruction, and Optimization.- Group 4: Quality Assurance in Model-Based Activities.

Simulation and Model-Based Methodologies: An Integrative View

This guide demonstrates how virtual build and test can be supported by the Discrete Event Systems Specification (DEVS) simulation modeling formalism, and the System Entity Structure (SES) simulation model ontology. The book examines a wide variety of Systems of Systems (SoS) problems, ranging from cloud computing systems to biological systems in agricultural food crops. Features: includes numerous exercises, examples and case studies throughout the text; presents a step-by-step introduction to DEVS concepts, encouraging hands-on practice to building sophisticated SoS models; illustrates virtual build and test for a variety of SoS applications using both commercial and open source DEVS simulation environments; introduces an approach based on activity concepts intrinsic to DEVS-based system design, that integrates both energy and information processing requirements; describes co-design modeling concepts and methods to capture separate and integrated software and hardware systems.

Guide to Modeling and Simulation of Systems of Systems

Introduction.- Part I: Modeling & simulation.- Part II: AI.- Part III: Software engineering.- Part IV: Collaborative/distributed computing.The complete table of contentscan be found on the Internet:http://www.springer.de

Discrete event modeling and simulation technologies : a tapestry of systems and AI-based theories and methodologies

DEVS (Discrete Event System Specification) is a general modelling formalism with sound semantics founded on a system theoretic basis. This gives it a claim to be universal for formalisms describing Discrete Event Dynamic Systems (DEDS). This gives that any other formalism such as Petri nets, which have become very popular for DEDS control can be embedded in it. Moreover, DEVS extends to the continuous case thus facilitating combined discrete/continuous modelling. The universality of DEVS is significant because it has been implemented in a variety of simulation environments, as extensions of diverse underlying Object-Oriented languages such as CLOS and C++. This gives it not only the power of formal rigor but also the practical capability of application to real world complex systems. The DEVS formalism has associated with it a characteristic abstract simulation engine architecture that can be realized in diverse sequential and parallel/distributed platforms. It is especially suitable for the simulation study of complex technical and natural systems with intelligent components and for long term model development capability acquired through systematic reusability. This tutorial will present the basic concepts of the DEVS formalism and its associated simulation methodology. These concepts will be illustrated in the context of large scale ecosystems modelling.

Bernard P. Zeigler

Papers

System entity structuring and model base management

Simulation and Model-Based Methodologies: An Integrative View

Guide to Modeling and Simulation of Systems of Systems

Discrete event modeling and simulation technologies : a tapestry of systems and AI-based theories and methodologies

DEVS formalism and methodology: unity of conception/diversity of application