Other affiliations: Pontifical Catholic University of Rio de Janeiro, University of London, University of Bologna
Bio: Franco Zambonelli is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Ubiquitous computing & Multi-agent system. The author has an hindex of 58, co-authored 449 publications receiving 13947 citations. Previous affiliations of Franco Zambonelli include Pontifical Catholic University of Rio de Janeiro & University of London.
Papers published on a yearly basis
TL;DR: It is argued that a multiagent system can naturally be viewed and architected as a computational organization, and the appropriate organizational abstractions that are central to the analysis and design of such systems are identified.
Abstract: 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.
TL;DR: The current state of autonomic communications research is surveyed and significant emerging trends and techniques are identified.
Abstract: Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.
TL;DR: This paper presents the TuCSoN coordination model for Internet applications based on network-aware and mobile agents, and shows how the adoption of TuC soN can positively benefit the design and development of such applications, firstly in general terms, then via a TuC SoN-coordinated sample application.
Abstract: The adoption of a powerful and expressive coordination model represents a key-point for the effective design and development of Internet applications In this paper, we present the TuCSoN coordination model for Internet applications based on network-aware and mobile agents, and show how the adoption of TuCSoN can positively benefit the design and development of such applications, firstly in general terms, then via a TuCSoN-coordinated sample application This is achieved by providing for an Internet interaction space made up of a multiplicity of independently programmable communication abstractions, called tuple centres, whose behaviour can be defined so as to embody the laws of coordination
TL;DR: This paper introduces three additional organisational concepts--organisational rules, organisational structures, and organisational patterns--that it believes are necessary for the complete specification of computational organisations.
Abstract: The architecture of a multi-agent system can naturally be viewed as a computational organisation. For this reason, we believe organisational abstractions should play a central role in the analysis and design of such systems. To this end, the concepts of agent roles and role models are increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts--organisational rules, organisational structures, and organisational patterns--that we believe are necessary for the complete specification of computational organisations.We view the introduction of these concepts as a step towards a comprehensive methodology for agent-oriented systems.
TL;DR: Some of the research issues, challenges and opportunities in the convergence between the cyber and physical worlds are presented, with a goal to stimulate new research activities in the emerging areas of CPW convergence.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).
01 Dec 1989
TL;DR: A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
Abstract: Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.
••01 Sep 2012
TL;DR: A survey of technologies, applications and research challenges for Internetof-Things is presented, in which digital and physical entities can be linked by means of appropriate information and communication technologies to enable a whole new class of applications and services.
Abstract: The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.
01 Jan 2003