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

Data Mining Practical Machine Learning Tools and Techniques

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

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Developing multiagent systems: The Gaia methodology

Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.

Developing Intelligent Agent Systems: A Practical Guide

Ambient assisted living (AAL) is focused on providing assistance to people primarily in their natural environment. Over the past decade, the AAL domain has evolved at a fast pace in various directions. The stakeholders of AAL are not only limited to patients, but also include their relatives, social services, health workers, and care agencies. In fact, AAL aims at increasing the life quality of patients, their relatives and the health care providers with a holistic approach. This paper aims at providing a comprehensive overview of the AAL domain, presenting a systematic analysis of over 10 
years of relevant literature focusing on the stakeholders’ needs, bridging the gap of existing reviews which focused on technologies. The findings of this review clearly show that until now the AAL domain neglects the view of the entire AAL ecosystem. Furthermore, the proposed solutions seem to be tailored more on the basis of the available existing technologies, rather than supporting the various stakeholders’ needs. Another major lack that this
 review is pointing out is a missing adequate evaluation of the various solutions. Finally, it seems that, as the domain of AAL is pretty new, it is still in its incubation phase. Thus, this review calls for moving the AAL domain to a more mature phase with respect to the research approaches.

Exploring the ambient assisted living domain: a systematic review

Multiple agent-oriented methodologies has been introduced in recent years, although only partial evaluations of these have been offered. As a result, it is difficult to select a methodology for a specific project. Additionally, there are no means for determining what the advantages and drawbacks of each methodology are. To resolve these problems, we devise a framework for evaluating and comparing agent-oriented methodologies. This framework focuses on four major aspects of a methodology: concepts and properties, notations and modelling techniques, process and pragmatics. We demonstrate the usage of the suggested framework by evaluating the GAIA methodology. This evaluation identifies the strengths and the weaknesses of GAIA, thus exemplifying the capabilities of our framework.

A Framework for Evaluating Agent-Oriented Methodologies

To develop agent- based systems, one needs a methodology that supports the development process as common in other disciplines. In recent years, several such methodologies and modeling techniques have been suggested. An important question is, to what extent do the existing methodologies address the developers' needs. In this paper we attempt to answer this question. In particular, we discuss suitability of agent modeling techniques to agent- based systems development. In evaluating existing modeling techniques, we address criteria from software engineering as well as characteristics of agent- based systems. Our evaluation shows that some aspects of modeling techniques for agent- based systems may benefit from further enhancements. As we show, these aspects include distribution, concurrency, testing and communication richness. We also find space for (relatively small) improvements in aspects such as the refining of the models throughout the development process and the coverage and consistency checking of the suggested models.

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Evaluation of modeling techniques for agent-based systems

While there is ample evidence that Multiagent Systems and Technologies (MAS&T) are vigorous as a research area, it is unclear what practical application impact this research area has accomplished to date. In this chapter, we describe method and results of a survey aiming at a comprehensive and up-to-date overview of deployed examples of MAS&T. We collected and analyzed 152 applications, covering important perspectives, such as ownership, maturity, vertical sectors, and usage of programming languages and agent platforms. We conclude that MAS&T have been successfully deployed in a significant number of applications, though mostly in what could be called niche markets. Off the spotlights of mass markets and current funding buzzwords, it appears that MAS&T is useful for various sectors. Note: This is a preliminary authors’ copy of a paper to appear in the forthcoming book Agent-Oriented Software Engineering edited by Onn Shehory and Arnon Sturm, to be published in the Springer Software Engineering book series, see http://www.springer.com/computer/swe/book/978-3-642-54431-6. Please use the citation information provided on the official Springer site to refer to this paper.

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Agent-Oriented Software Engineering

Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.

Arnon Sturm

Papers

Exploring the ambient assisted living domain: a systematic review

A Framework for Evaluating Agent-Oriented Methodologies

Evaluation of modeling techniques for agent-based systems

Agent-Oriented Software Engineering

The challenge of real-time multi-agent systems for enabling IoT and CPS