Extending Answer Set Programming using Generalized Possibilistic Logic

This international workshop is one of the joint ontology workshops JOWO 2015 affiliated with the 24th International Joint Conference on Artificial Intelligence (IJCAI-2015)International audienceAnswer set programming (ASP) is a form of logic programming in which negation-as-failure is defined in a purely declarative way, based on the notion of a stable model. This short paper briefly explains how a recent generalization of possibilistic logic (GPL) can be used to characterize the semantics of answer set programming. This characterization has several advantages over existing characterizations of the stable model semantics. First, unlike reduct-based approaches, it does not rely on a syntactic procedure: we can directly characterize answer sets based on the minimally specific models of a GPL theory. Second, GPL enables us to study extensions of ASP in an intuitive way: unlike in existing generalizations of ASP such as equilibrium logic and autoepistemic logic, all formulas in GPL have a meaning which is intuitively clear. Finally, being based on possibilistic logic, GPL offers a natural way of dealing with uncertainty in answer set programs

Advancing the Boundaries of Formal Argumentation: Reflections on the AI3 2021 Special Issue

This article reflects on the Special Issue based on invited papers from the 5th Workshop on Advances in Argumentation in Artificial Intelligence (AI3 2021), showcasing the latest advancements in the field made by the Italian community on argumentation, as well as other researchers worldwide. This Special Issue highlights the importance of advancing logical-based AI approaches, such as formal argumentation, in the continuously expanding landscape of Artificial In- telligence. Papers in this Special Issue cover a diverse range of topics, including argument game-based proof theories, analysis of legal cases, decomposability in abstract argumentation, meta-argumentation approaches, explanations for model outputs using causal models, representation of natural argumentative discourse, and Paraconsistent Weak Kleene logic-based belief revision. By em- phasizing these innovative research contributions, this article underscores the need for continued progress in the field of Formal Argumentation to complement and enhance the ongoing developments in AI

Challenges for a CBR framework for argumentation in open MAS

[EN] Nowadays, Multi-Agent Systems (MAS) are broadening their applications to open environments, where heterogeneous agents could enter into the system, form agents’ organizations and interact. The high dynamism of open MAS gives rise to potential conflicts between agents and thus, to a need for a mechanism to reach agreements. Argumentation is a natural way of harmonizing conflicts of opinion that has been applied to many disciplines, such as Case-Based Reasoning (CBR) and MAS. Some approaches that apply CBR to manage argumentation in MAS have been proposed in the literature. These improve agents’ argumentation skills by allowing them to reason and learn from experiences. In this paper, we have reviewed these approaches and identified the current contributions of the CBR methodology in this area. As a result of this work, we have proposed several open issues that must be taken into consideration to develop a CBR framework that provides the agents of an open MAS with arguing and learning capabilities.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under TIN2006-14630-C0301 project.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2009). Challenges for a CBR framework for argumentation in open MAS. Knowledge Engineering Review. 24(4):327-352. https://doi.org/10.1017/S0269888909990178S327352244Willmott S. , Vreeswijk G. , Chesñevar C. , South M. , McGinnis J. , Modgil S. , Rahwan I. , Reed C. , Simari G. 2006. Towards an argument interchange format for multi-agent systems. In Proceedings of the AAMAS International Workshop on Argumentation in Multi-Agent Systems, ArgMAS-06, 17–34.Sycara, K. P. (1990). Persuasive argumentation in negotiation. 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In 8th World Conference of the Artificial Intelligence in Education Society, 87–94.Rahwan, I. (2005). Guest Editorial: Argumentation in Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 11(2), 115-125. doi:10.1007/s10458-005-3079-0RISSLAND, E. L., ASHLEY, K. D., & BRANTING, L. K. (2005). Case-based reasoning and law. The Knowledge Engineering Review, 20(3), 293-298. doi:10.1017/s0269888906000701Tolchinsky, P., Cortes, U., Modgil, S., Caballero, F., & Lopez-Navidad, A. (2006). Increasing Human-Organ Transplant Availability: Argumentation-Based Agent Deliberation. IEEE Intelligent Systems, 21(6), 30-37. doi:10.1109/mis.2006.116McBurney, P., Hitchcock, D., & Parsons, S. (2006). The eightfold way of deliberation dialogue. International Journal of Intelligent Systems, 22(1), 95-132. doi:10.1002/int.20191Rissland, E. L., Ashley, K. D., & Loui, R. P. (2003). AI and Law: A fruitful synergy. 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Variations on a Theme: A Bibliography on Approaches to Theorem Proving Inspired From Satchmo

This articles is a structured bibliography on theorem provers, approaches to theorem proving, and theorem proving applications inspired from Satchmo, the model generation theorem prover developed in the mid 80es of the 20th century at ECRC, the European Computer- Industry Research Centre. Note that the bibliography given in this article is not exhaustive