Fausto Giunchiglia

Bio: Fausto Giunchiglia is an academic researcher from University of Trento. The author has contributed to research in topics: Computer science & Ontology (information science). The author has an hindex of 54, co-authored 382 publications receiving 17490 citations. Previous affiliations of Fausto Giunchiglia include University of Edinburgh & University of Genoa.

Tropos: An Agent-Oriented Software Development Methodology

Abstract: Our goal in this paper is to introduce and motivate a methodology, called Tropos,1 for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all related mentalistic notions (for instance goals and plans) are used in all phases of software development, from early analysis down to the actual implementation. Second, Tropos covers also the very early phases of requirements analysis, thus allowing for a deeper understanding of the environment where the software must operate, and of the kind of interactions that should occur between software and human agents. The methodology is illustrated with the help of a case study. The Tropos language for conceptual modeling is formalized in a metamodel described with a set of UML class diagrams.

NuSMV 2: An OpenSource Tool for Symbolic Model Checking

Abstract: This paper describes version 2 of the NuSMV tool. NuSMV is a symbolic model checker originated from the reengineering, reimplementation and extension of SMV, the original BDD-based model checker developed at CMU [15]. The NuSMV project aims at the development of a state-of-the-art symbolic model checker, designed to be applicable in technology transfer projects: it is a well structured, open, flexible and documented platform for model checking, and is robust and close to industrial systems standards [6].

NuSMV 2: An opensource tool for symbolic model checking

Abstract: This paper describes version 2 of the NuSMV tool. NuSMV is a symbolic model checker originated from the reengineering, reimplementation and extension of SMV, the original BDD-based model checker developed at CMU [15]. The NuSMV project aims at the development of a state-of-the-art symbolic model checker, designed to be applicable in technology transfer projects: it is a well structured, open, flexible and documented platform for model checking, and is robust and close to industrial systems standards [6].

NUSMV: a new symbolic model checker

Abstract: This paper describes a new symbolic model checker, called NuSMV, developed as part of a joint project between CMU and IRST. NuSMV is the result of the reengineering, reimplementation and, to a limited extent, extension of the CMU SMV model checker. The core of this paper consists of a detailed description of the NuSMV functionalities, architecture, and implementation.

NUSMV: A New Symbolic Model Verifier

Abstract: This paper describes NUSMV, a new symbolic model checker developed as a joint project between Carnegie Mellon University (CMU) and Istituto per la Ricerca Scientifica e Tecnolgica (IRST) NUSMV is designed to be a well structured, open, flexible and documented platform for model checking In order to make NUSMV applicable in technology transfer projects, it was designed to be very robust, close to the standards required by industry, and to allow for expressive specification languages NUSMV is the result of the reengineering, reimplementation and extension of SMV [6], version 244 (SMV from now on) With respect to SMV, NUSMV has been extended and upgraded along three dimensions First, from the point of view of the system functionalities, NUSMV features a textual interaction shell and a graphical interface, extended model partitioning techniques, and allows for LTL model checking Second, the system architecture of NUSMV has been designed to be highly modular and open The interdependencies between different modules have been separated, and an external, state of the art BDD package [8] has been integrated in the system kernel Third, the quality of the implementation has been strongly enhanced This makes of NUSMV a robust, maintainable and well documented system, with a relatively easy to modify source code NUSMV is available at http://nusmvirstitcit/

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

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.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.