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  1. (1 other version)Probabilistic abstract argumentation: an investigation with Boltzmann machines.Régis Riveret, Dimitrios Korkinof, Moez Draief & Jeremy Pitt - 2015 - Argument and Computation 6 (2):178-218.
    Probabilistic argumentation and neuro-argumentative systems offer new computational perspectives for the theory and applications of argumentation, but their principled construction involves two entangled problems. On the one hand, probabilistic argumentation aims at combining the quantitative uncertainty addressed by probability theory with the qualitative uncertainty of argumentation, but probabilistic dependences amongst arguments as well as learning are usually neglected. On the other hand, neuro-argumentative systems offer the opportunity to couple the computational advantages of learning and massive parallel computation from neural networks (...)
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  • On searching explanatory argumentation graphs.Régis Riveret - 2020 - Journal of Applied Non-Classical Logics 30 (2):123-192.
    Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. (...)
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  • A neural cognitive model of argumentation with application to legal inference and decision making.Artur S. D'Avila Garcez, Dov M. Gabbay & Luis C. Lamb - 2014 - Journal of Applied Logic 12 (2):109-127.
    Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used (...)
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  • Establishing norms with metanorms in distributed computational systems.Samhar Mahmoud, Nathan Griffiths, Jeroen Keppens, Adel Taweel, Trevor J. M. Bench-Capon & Michael Luck - 2015 - Artificial Intelligence and Law 23 (4):367-407.
    Norms provide a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. One of the most influential formulations of norm emergence was proposed by Axelrod :1095–1111, 1986). This paper provides an empirical analysis of aspects of Axelrod’s approach, by exploring some of the key assumptions made in previous evaluations of the model. We explore the dynamics of norm emergence and the occurrence of norm collapse when applying the model over extended durations. It (...)
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  • The pursuit of computational justice in open systems.Jeremy Pitt, Dídac Busquets & Régis Riveret - 2015 - AI and Society 30 (3):359-378.
    Many open networks, distributed computing systems, and infrastructure management systems face a common problem: how to distribute a collectivised set of resources amongst a set of autonomous agents of heterogenous provenance. One approach is for the agents themselves to self-organise the allocation of resources with respect to a set of agreed conventional rules; but given an allocation scheme which maps resources to those agents and a set of rules for determining that allocation scheme, some natural questions arise—Is this allocation fair? (...)
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