471,689 research outputs found

    A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

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    The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

    Deep Reinforcement Learning for Multi-Agent Interaction

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    The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.Comment: Published in AI Communications Special Issue on Multi-Agent Systems Research in the U

    Understanding inter-organizational decision coordination

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    This article develops a theoretical framework to investigate the interaction and coordination of decision-making processes in a supply chain with multiple and inter-dependent suppliers and customers. Design/Methodology/Approach: Three longitudinal case studies on the decision coordination processes between a European toy supplier and three retailers. Findings: The case studies found different mental models, decision-making behaviours, coordination behaviours and ordering behaviours even though the toy supplier and the three retailers observed quite the same material flow behaviours. The study found explanations for these diverse behaviours by analyzing the mental models and decision-making behaviours of each involved party. Originality/value: The findings explain the conditions which lead to undesirable mental models and decision-making behaviours which affect the coordination of decisions among supply chain members

    Preface to the Special Issue on Advances in Argumentation in Artificial Intelligence

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    Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as evident. The study of argumentation has been the focus of attention of philosophers and scholars, from Aristotle and classical rhetoric to the present day. The computational study of arguments has emerged as a field of research in AI in the last two decades, mainly fuelled by the interest from scholars in logics, non-monotonic and epistemic reasoning, and in related disciplines such as Law, Sociology and Computational Linguistics. This special issue collects a selection of five papers from the 2nd Workshop on Advances In Argumentation In Artificial Intelligence, co-located with AI*IA 2018, the 17th International Conference of the Italian Association for Artificial Intelligence held in Trento in November 2018. The workshop was organized as part of the activities of the Argumentation in Artificial Intelligence Working Group. The Argumentation Group is a working group of the Associazione Italiana per l’Intelligenza Artificiale (AI*IA) whose general goal is to promote Italian scientific activities in the field of Argumentation in Artificial Intelligence, and foster collaborations between research groups. The selected papers discuss theoretical foundations in argumentation as well as challenges and real-world problems for which argumentation may represent a viable AI-paradigm. Each submission underwent a single-blind peer-review process and the five accepted articles were reviewed by at least two independent expert reviewers. Much work in computational models of argument is centered on Dung’ seminal 1995 paper “On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.”. On the one hand, this is reflected by the papers presented in this special issue, with four out of five papers describing works directly linked to Dung’s abstract framework or to its extensions. On the other hand, the papers also testify the variety and richness of the current state-of-the-art of argumentation studies, which extends and goes far beyond Dung’s work, proposing research combining natural language processing and probabilistic reasoning with abstract argumentation The papers by Flesca, Dondio and Longo, and Taticchi and Bistarelli are theoretical works in the area of computational argumentation. The paper by Flesca examines the problem of efficiently computing the probability of the extensions of bipolar probabilistic argumentation frameworks, proposing a set of more efficient and empirically-tested algorithms. The paper by Dondio and Longo introduces a novel abstract argumentation semantics. Inspired by the ambiguity blocking semantics of defeasible logic, the authors propose a semantics where the undecided label assigned to some arguments could be blocked instead of being propagated to attacked arguments. The paper by Taticchi and Bistarelli proposes a cooperative-game approach to share acceptability and rank arguments of an argumentation framework. The paper by Gobbo et al. proposes a new method for annotating arguments expressed in natural language, called adpositional argumentation. By doing so, they provide the guidelines for designing a gold standard corpus that could benefit studies in argumentation mining and arguments definition. The paper by Pazienza et al. proposes an interesting application of abstract argumentation to financial predictions. The authors design a framework combining natural language processing along with abstract argumentation techniques to automatically extract relevant arguments from Earning Conference Call transcripts, weight such arguments and produce a final advice aimed to anticipate and predict analysts’ recommendations. Finally, the Editors are like to acknowledge the work of the members of the Programme Committee whose invaluable expertise and efforts have led to the selection of the papers included in this special issue. Last but not least, the editors would like to thank all the authors that have contributed to this special issue

    The challenge of complexity for cognitive systems

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    Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

    Multi-agent simulations for emergency situations in an airport scenario

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    This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

    Identity and Commitment: Sen\u27s Fourth Aspect of the Self

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    Human Conscious Experience is Four-Dimensional and has a Neural Correlate Modeled by Einstein's Special Theory of Relativity

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    In humans, knowing the world occurs through spatial-temporal experiences and interpretations. Conscious experience is the direct observation of conscious events. It makes up the content of consciousness. Conscious experience is organized in four dimensions. It is an orientation in space and time, an understanding of the position of the observer in space and time. A neural correlate for four-dimensional conscious experience has been found in the human brain which is modeled by Einstein’s Special Theory of Relativity. Spacetime intervals are fundamentally involved in the organization of coherent conscious experiences. They account for why conscious experience appears to us the way it does. They also account for assessment of causality and past-future relationships, the integration of higher cognitive functions, and the implementation of goal-directed behaviors. Spacetime intervals in effect compose and direct our conscious life. The relativistic concept closes the explanatory gap and solves the hard problem of consciousness (how something subjective like conscious experience can arise in something physical like the brain). There is a place in physics for consciousness. We describe all physical phenomena through conscious experience, whether they be described at the quantum level or classical level. Since spacetime intervals direct the formation of all conscious experiences and all physical phenomena are described through conscious experience, the equation formulating spacetime intervals contains the information from which all observable phenomena may be deduced. It might therefore be considered expression of a theory of everything

    Taking Free Will Skepticism Seriously

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    An apparently increasing number of philosophers take free will skepticism to pose a serious challenge to some of our practices. This must seem odd to many—why should anyone think that free will skepticism is relevant for our practices, when nobody seems to think that other canonical forms of philosophical skepticism are relevant for our practices? Part of the explanation may be epistemic, but here I focus on a metaethical explanation. Free will skepticism is special because it is compatible with ‘basic moral reasons’—moral reasons acknowledged by all mainstream ethicists—and other minds and induction skepticism are not. One example is our reason not to intentionally harm others. Practical seriousness about other minds and induction skepticism undermines this reason, but practical seriousness about free will skepticism only undermines a potential overrider of this reason, that is, the reason of retribution
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