5,586 research outputs found

    Intentional dialogues in multi-agent systems based on ontologies and argumentation

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    Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence

    Defeasible Logic Programming: An Argumentative Approach

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    The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, ie, the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.Comment: 43 pages, to appear in the journal "Theory and Practice of Logic Programming

    Proceedings of the 11th European Agent Systems Summer School Student Session

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    This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

    Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming

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    In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI
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