We investigate the argumentation frameworks (AFs) that arise from multi-player transferable-utility cooperative games. These AFs have uncountably infinitely many arguments; arguments represent alternative payoff distributions to the players. We examine which of the various properties of AFs (from Dung's 1995 seminal paper) hold; we prove that these AFs are never finitary, never well-founded, always controversial and never limited controversial. We hope that this will encourage further exchange of ideas between argumentation and cooperative games.

We apply ideas from abstract argumentation theory to study cooperative game theory. Building on Dung's results in his seminal paper, we further the correspondence between Dung's four argumentation semantics and solution concepts in cooperative game theory by showing that complete extensions (the grounded extension) correspond to Roth's subsolutions (respectively, the supercore). We then investigate the relationship between well-founded argumentation frameworks and convex games, where in each case the semantics (respectively, solution concepts) coincide; we prove that three-player convex games do not in general have well-founded argumentation frameworks.

We apply ideas from abstract argumentation theory to study cooperative game theory. Building on Dung’s results in his seminal paper, we further the correspondence between Dung’s four argumentation semantics and solution concepts in cooperative game theory by showing that complete extensions (the grounded extension) correspond to Roth’s subsolutions (respectively, the supercore). We then investigate the relationship between well-founded argumentation frameworks and convex games, where in each case the semantics (respectively, solution concepts) coincide; we prove that three-player convex games do not in general have well-founded argumentation frameworks.

We consider two teams of agents engaging in a debate to persuade an audience of the acceptability of a central argument. This is modelled by a bipartite abstract argumentation framework with a distinguished topic argument, where each argument is asserted by a distinct agent. One partition defends the topic argument and the other partition attacks the topic argument. The dynamics are based on flag coordination games: in each round, each agent decides whether to assert its argument based on local knowledge. The audience can see the induced sub-framework of all asserted arguments in a given round, and thus the audience can determine whether the topic argument is acceptable, and therefore which team is winning. We derive an analytical expression for the probability of either team winning given the initially asserted arguments, where in each round, each agent probabilistically decides whether to assert or withdraw its argument given the number of attackers.

We consider a one-to-many persuasion setting, where a persuader presents arguments to a multi-party audience, aiming to convince them of some particular goal argument. The individual audience members each have differing personal knowledge, which they use, together with the arguments presented by the persuader, to determine whether they are convinced of the goal. The persuader must, therefore, carefully consider its strategy, i.e., which arguments to assert, in order to maximise the number of convinced audience members. Here, we use evolutionary search to find (near-)optimal strategies for the persuader. We implement our approach using search-based model engineering, which provides a natural and efficient encoding for such problems. We investigate the performance of our approach on a range of settings, considering different structures and sizes of argumentation frameworks (representing the underlying knowledge available to the persuader and audience members), and varying the size of audience and of the audience members' personal knowledge bases. We show that we can find effective strategies for problems with more than 200 arguments and more than 100 audience members. Further, we show that the approach supports multiple persuader objectives, finding persuader strategies that aim to minimise arguments to assert while still maximising the number of convinced audience members.

It is well known that the computation of solutions to decision and enumeration problems in argumentation can be very hard. In this work, we analyse some of the results of the 2017 International Competition on Computational Models of Argumentation. Our analysis shifts the focus from the performance of individual solvers to how well/badly they can collectively tackle different classes of abstract argumentation frameworks. In so doing, we were able to identify the instances that were particularly difficult for all/most solvers and look into their particular structural properties.

We investigate the relationship between the structural properties of argumentation frameworks and their argument-based characteristics, examining the characteristics of structures of Dung-style frameworks and two generalisations: extended argumentation frameworks and collective-attack frameworks. Our results show that the structural properties of frameworks have an impact on the size of extensions produced, on the proportion of subsets of arguments that determine some topic argument to be acceptable, and on the likelihood that the addition of some new argument will affect the acceptability of an existing argument, all characteristics that are known to affect the performance of argumentation-based technologies. We demonstrate the applicability of our results with two case studies.

Argument-based persuasion dialogues provide an effective mechanism for agents to communicate their beliefs, and their reasons for those beliefs, in order to convince another agent of some topic argument. In such dialogues, the persuader has strategic considerations, and must decide which of its known arguments should be asserted, and the order in which they should be asserted. Recent works consider mechanisms for determining an optimal strategy for persuading the responder. However, computing such strategies is expensive, swiftly becoming impractical as the number of arguments increases. In response, we present a strategy that uses heuristic information of the domain arguments and can be computed with high numbers of arguments. Our results show that not only is the heuristic strategy fast to compute, it also performs significantly better than a random strategy.

Argument-based deliberation dialogues are an important mechanism in the study of agent coordination, allowing agents to exchange formal arguments to reach an agreement for action. Agents participating in a deliberation dialogue may begin the dialogue with very similar sets of arguments to one another, or they may start the dialogue with disjoint sets of arguments, or some middle ground. In this paper, we empirically investigate whether the similarity of agents’ arguments affects the dialogue outcome. Our results show that agents that have similar sets of initially known arguments are less likely to reach an agreement through dialogue than those that have dissimilar sets of initially known arguments.