Institute for Web Science and Technologies · Universität Koblenz - Landau
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Implementing Control Argumentation Framework using Answer Set Programming

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Argumentation is a highly researched topic in the area of reasoning. However the central formalism of Dung abstract argumentation frameworks (AF) [1] can not handle uncertainty. So the model control argumentation framework (CAF) was introduced, which proposed as a unifying framework to capture uncertainties about the existence of arguments and attacks between arguments. Control argumentation frameworks also feature uncertain arguments and attacks and are intended specifically to model strategic scenarios from the point of view of an agent. The model divides the uncertain elements of an argumentation into a control part and an uncertain part, where the existence of elements in the control part is controlled by the agent, and the existence of elements in the uncertain part is not. The problem of controllability in control argumentation frameworks asks whether there exists a selection of arguments in the control part, such that for all selections of elements in the uncertain part a given target set of arguments satisfies a spectified set of properties. This problem was analyzed by Dimopoulos et al. [2] and Niskanen et al. [3].

The goal of this thesis is to implement CAFs into the TweetyProject library [4] as well as a solver for the controllability problem for a specified set of properties. This solver should use Answer Set Programming and an evalutation is expected.


[1] Dung, Phan Minh. “On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.” Artificial intelligence 77.2 (1995): 321-357.[2] Yannis Dimopoulos, Jean-Guy Mailly, and Pavlos Moraitis. Control argumentation frameworks. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 4678–4685, 2018.[3] Andreas Niskanen, Daniel Neugebauer, and Matti Järvisalo. Controllability of contr argu mentation frameworks. In Proceedings of the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI- PRICAI 2020), International, 2020. International Joint Conferences on Artificial Intelligence.[4] Thimm, Matthias. “The tweety library collection for logical aspects of artificial intelligence and knowledge representation.” KI-Künstliche Intelligenz 31.1 (2017): 93-97.


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