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

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 as well as those that are more likely to adopt logic-based approaches in the future

A Framework for Combining Defeasible Argumentation with Labeled Deduction

In the last years, there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. Defeasible argumentation has proven to be a successful approach to formalizing commonsense reasoning, encompassing many other alternative formalisms for defeasible reasoning. Argument-based frameworks share some common notions (such as the concept of argument, defeater, etc.) along with a number of particular features which make it difficult to compare them with each other from a logical viewpoint. This paper introduces LDSar, a LDS for defeasible argumentation in which many important issues concerning defeasible argumentation are captured within a unified logical framework. We also discuss some logical properties and extensions that emerge from the proposed framework.Comment: 15 pages, presented at CMSRA Workshop 2003. Buenos Aires, Argentin

A Formal Framework for Designing Boundedly Rational Agents

Notions of rationality and bounded rationality play important roles in research on the design and implementation of autonomous agents and multi-agent systems, for example in the context of instilling socially intelligent behavior into computing systems. However, the (formal) connection between artificial intelligence research on the design and implementation of boundedly rational and socially intelligent agents on the one hand and formal economic rationality – i.e., choice with clear and consistent preferences – or instrumental rationality – i.e., the maximization of a performance measure given an agent’s knowledge – on the other hand is weak. In this paper we address this shortcoming by introducing a formal framework for designing boundedly rational agents that systematically relax instrumental rationality, and we propose a system architecture for implementing such agents

A Formal Framework for Designing Boundedly Rational Agents

Notions of rationality and bounded rationality play important roles in research on the design and implementation of autonomous agents and multi-agent systems, for example in the context of instilling socially intelligent behavior into computing systems. However, the (formal) connection between artificial intelligence research on the design and implementation of boundedly rational and socially intelligent agents on the one hand and formal economic rationality – i.e., choice with clear and consistent preferences – or instrumental rationality – i.e., the maximization of a performance measure given an agent’s knowledge – on the other hand is weak. In this paper we address this shortcoming by introducing a formal framework for designing boundedly rational agents that systematically relax instrumental rationality, and we propose a system architecture for implementing such agents

Emotions on agent based simulators for group formation

Time and space consuming are key factors in a meeting, and therefore must be object of consideration in any process of socialization. So, group decision simulation could be a valuable training tool, through which it will be possible to create and test virtual group decision scenarios. In this work we propose a multi-agent simulator of group decision making that models the participant cortex by considering its emotional states and the exchange of arguments among them.Fundação para a Ciência e a Tecnologia (FCT) - ArgEmotionAgents Project (POSI/EIA/56259/2004)

Когнитивни процеси, емоции и интелигентни интерфејси

Студијата презентира истражувања од повеќе научни дисциплини, како вештачка интелигенција, невронауки, психологија, лингвистика и филозофија, кои имаат потенцијал за креирање на интелигентни антропоморфни агенти и интерактивни технологии. Се разгледуваат системите од симболичка и конекционистичка вештачка интелигенција за моделирање на човековите когнитивни процеси, мислење, донесување одлуки, меморија и учење. Се анализираат моделите во вештачка интелигенција и роботика кои користат емоции како механизам за контрола на остварување на целите на роботот, како реакција на одредени ситуации, за одржување на процесот на социјална интеракција и за создавање на поуверливи антропормфни агенти. Презентираните интердисциплинарни методологии и концепти се мотивација за создавање на анимирани агенти кои користат говор, гестови, интонација и други невербални модалитети при конверзација со корисниците во интелигентните интерфејси

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17