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

An Introduction to MultiAgent Systems.

What is modal logic

When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not

/pdf/a-review-of-multiobjective-test-problems-and-a-scalable-test-5e7abjapzg.pdf

A review of multiobjective test problems and a scalable test problem toolkit

Software systems become more and more complex thus the application of self-developing distributed and decentralized processing is indispensable. The complexity of such systems requires new tools for designing, programming and debugging processes which imp|ies the fact that new approaches to decentralization should be unde.rtaken. An idea of autonomous agents arises as an extension to the object and process concepts. The active agent is invented as a basic element of which distributed and decentralized systems can be built. The use of evolution strategies in design of multi-ageat systems reveals new posibilities of developing complex software systems. "rh(: cwJlution plays also a key role in creation and organi~.ation of social structures. In this paper a new te(:hnology of designing and building agent sy~ terns based on genetic methods and a draft concept of a model-based approach to such systems are described. Also an application of this technology to a self-developing prediction system is presented and results of simulation experiments carried out with the use of I)-1 random time series are discussed.

/pdf/the-application-of-evolution-process-in-multi-agent-world-to-ifm7cw4i1b.pdf

The Application of Evolution Process in Multi-Agent World to the Prediction System

The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

/pdf/evolutionary-multi-agent-systems-9j3hywl5cl.pdf

Evolutionary multi-agent systems

Security, energy, and performance-aware resource allocation mechanisms for computational grids

The paper deals with a specific class of multi-agent systems, in principle similar to evolutionary algorithms, but utilising a more complex, since decentralised, model of evolution. The proposed layered architecture uses the notion of a profile that models strategies and goals of an agent with respect to some aspect of its operation. The paper presents main ideas of the architecture illustrated by a concrete realisation that is an evolutionary multi-agent system solving a generic optimisation problem.

Agent-Oriented Model of Simulated Evolution

This work presents a new evolutionary approach to searching for a global solution (in the Pareto sense) to a multiobjective optimisation problem. The novelty of the method proposed consists in the application of an evolutionary multi-agent system (EMAS) instead of classical evolutionary algorithms. Decentralisation of the evolution process in EMAS allows for intensive exploration of the search space, and the introduced mechanism of crowd allows for effective approximation of the whole Pareto frontier. In the paper the technique is described as well as preliminary experimental results are reported.

/pdf/agent-based-evolutionary-multiobjective-optimisation-10g9aoy6u4.pdf

Marek Kisiel-Dorohinicki

Papers

The Application of Evolution Process in Multi-Agent World to the Prediction System

Evolutionary multi-agent systems

Security, energy, and performance-aware resource allocation mechanisms for computational grids

Agent-Oriented Model of Simulated Evolution

Agent-based evolutionary multiobjective optimisation