. The third International Competition on Computational Models of Argumentation (ICCMA’19) focuses on reasoning tasks in Abstract Argumentation. Submitted solvers are tested on a selected collection of benchmark instances, including artificially generated argumentation frameworks and some frameworks formalizing real-world problems. In this paper we introduce the testing environment set for the competition, including its problems and participants.
The Trust-aware Abstract Argumentation Frameworks (TAAFs) have been proposed in [18] as a variant of the well-known abstract argumentation frameworks where the trustworthiness of the agents participating the dispute is taken into account. In particular, T-AAFs consist in AAFs where arguments are associated with weights derived from the trust degrees of the agents proposing them. [18] studies the problem min-Tver (resp., min-Tacc) of computing the minimum trust degree τ∗ such that, if the arguments said only by agents whose trust degree is not greater than τ∗ are discarded, a given set of arguments S (resp., argument a), that is not necessarily an extension (resp., (credulously) accepted) over the original argumentation framework, becomes an extension (resp., (credulously) accepted). We extend the proposal in [18] by devising suitable methods for solving the problems min-Tver and min-Tacc. Specifically, we provide a translation for the intractable cases of min-Tver and min-Tacc into instances of Integer Linear Programming (ILP), so that they can be solved by resorting to standard ILP solvers.
Many of the conversations we have every day involve exchanges of arguments and counteraguments. In the context of artificial intelligence and argumentation theory, such phenomena fall into the area of dialogical argumentation. Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. We can assume that a chatbot would take a particular stance in the dialogue, opposing the stance of the user. In order to succeed, the chatbot also needs to be aware of various arguments and the interplay between them. Such knowledge can be represented by a directed graph, where nodes stand for arguments and arcs symbolise conflicts between them. The chatbot must be aware of both sides of the discussion, i.e. the arguments that it can play as well as ones that the user might have, to be able to formulate convincing responses. The availability of large argument graphs for research, however, is very limited. This means that researchers do not have corpora available which hinders the development of new chatbots and limits the e↵ectiveness of existing ones. In this paper, we propose a method to acquire a large number of arguments in a graph structure using crowd sourcing. We evaluate this method in a study with participants and present a corpus which can be used for further research in computational argumentation and chatbot technologies for argumentation.
