Introduction to Annotated Logics (original) (raw)

The Role of Annotated Logics in Ai: A Review Paper

Journal of Computer Science and Cybernetics, 2021

Annotated Logics are a category of non-classical logics that have recently appeared from a historical point of view. They are a type of paraconsistent, paracomplete and non-alethic logic. With the rapid development of AI and Automation and Robotics, more and more theory and techniques were coined to support the various issues that the themes were presenting. This expository work explores how to deal directly with conflicts (contradictions) and paracompleteness directly, without extra-logical devices. Support is given by the paraconsistent annotated evidential logic Et. Some applications are discussed.

Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives

Information

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches ...

Logics in Artificial Intelligence: 9th European Conference, JELIA 2004, Lisbon, Portugal, September 27-30, 2004, Proceedings

2004

Logics have, for many years, laid claim to providing a formal basis for the study of artificial intelligence. With the depth and maturity of methodologies, formalisms, procedures, implementations, and their applications available today, this claim is stronger than ever, as witnessed by increasing amount and range of publications in the area, to which the present proceedings accrue. The European series of Workshops on Logics in Artificial Intelligence (or Journées Européennes sur la Logique en Intelligence Artificielle-JELIA) began in response to the need for a European forum for the discussion of emerging work in this burgeoning field. JELIA 2000 is the seventh such workshop in the

Logic in Artificial Intelligence and Industrial Applications, Part I

There are many logical systems that depart from classical logic. These systems can be divided into two groups: those that extend classical logic and those that rival it. The first group can be further divided into two categories: The first category encompasses modal logic, epistemic/doxastic logics and temporal logics. The second category includes nonmonotonic logics, which are extensions of classical logic in some untraditional way. The second group includes many-valued logics and fuzzy logic. In this paper (Part 1) we give a presentation of classical logic and some logics in the first category. We indicate for each logical system some of its applications In Obeid (2001), which appears in the next issue of this journal, we present some nonmonotonic logics and some of the rival logics that are both interesting and promising.

Logic Programming for Intelligent Systems

Encyclopedia of Information Science and Technology, Fourth Edition, 2018

In what seem to be never-ending quests for automation, integration, seamlessness, new genres of applications, and “smart systems”, all of which are fueled in part by technological changes, intellectual maturity (or so one thinks), and out-of-the-box thinking that says “surely, there must be a better way”, one dreams of a future. This paper suggests that logic programs employing recent advances in semantics and in knowledge representation formalisms provide a more robust framework in which to develop very intelligent systems in any domain of knowledge or application. The author has performed work applying this paradigm and these reasoning formalisms in the areas of financial applications, security applications, and enterprise information systems.

Logics for Computer and Data Sciences, and Artificial Intelligence

Studies in Computational Intelligence, 2022

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.

Introduction to Intelligent Systems

The Industrial Electronics Handbook – Intelligent Systems, 2011

The text presented here served as an introduction to the book “Intelligent Systems” - the first part of the five-volume series entitled The Industrial Electronics Handbook. Numerous intelligent systems, described and discussed in the subsequent chapters of the book, are based on different approaches to machine intelligence problems. The authors of these chapters show the necessity of using various methods for building intelligent systems. Almost every particular problem needs an individual solution; thus, we can study many different intelligent systems reported in the literature. This chapter is a kind of introduction to particular systems and different approaches presented in the book. The role of this chapter is to provide the reader with a bird's eye view of the area of intelligent systems. Before we explain what intelligent systems are and why it is worth to study and use them, it is necessary to comment on one problem connected with the terminology. The problems of equipping artificial systems with intelligent abilities are, in fact, unique. We always want to achieve a general goal, which is a better operation of the intelligent system than one, which can be accomplished by a system without intelligent components. There are many ways to accomplish this goal and, therefore, we have many kinds of artificial intelligent (AI) systems. In general, it should be stressed that there are two distinctive groups of researches working in these areas: the AI community and the computational intelligence community. he goal of both groups is the same: the need for artificial systems powered by intelligence. However, different methods are employed to achieve this goal by different communities.