This paper addresses the problem of task allocation among multiple autonomous agents that must accomplish a complex global task. Solutions to the problem have real-world applications in defense, space, disaster management, etc. We solve this problem via agent coalition formation. Multiple coalition formation mechanisms were introduced in prior art, seldom accounting for interdependent tasks. We address this challenge. We introduce an anytime decentralized coalition formation mechanism that enables agents with complementary capabilities to form, autonomously and dynamically, feasible coalition structures that accomplish a global, composite task. The formed structures are incrementally improved via agent replacements to optimize a global utility. We analyze the complexity and show that, although the general problem is NP-hard, our mechanism provides a solution within acceptable time. We present extensive experimental results that illustrate the added value of our approach.

We review a recently introduced model in which each of a number of agents is endowed with an abstract argumentation framework reflecting her individual views regarding a given set of arguments. A question arising in this context is whether the diversity of views observed in such a situation is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual information and, second, the personal preferences of the agent concerned. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. This is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

Different agents may have different points of view. Following a popular approach in the artificial intelligence literature, this can be modeled by means of different abstract argumentation frameworks, each consisting of a set of arguments the agent is contemplating and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned regarding the moral or social values the arguments under scrutiny relate to. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. In doing so, we distinguish different constraints on admissible rationalisations, e.g., concerning the types of preferences used or the number of distinct values involved. We also distinguish two different semantics for rationalisability, which differ in the assumptions made on how agents treat attacks between arguments they do not report. This research agenda, bringing together ideas from abstract argumentation and social choice, is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

Argumentation is a process of evaluating and comparing a set of arguments. A way to compare them consists in using a ranking-based semantics which rank-order arguments from the most to the least acceptable ones. Recently, a number of such semantics have been pro- posed independently, often associated with some desirable properties. However, there is no comparative study which takes a broader perspective. This is what we propose in this work. We provide a general comparison of all these semantics with respect to the proposed proper- ties. That allows to underline the differences of behavior between the existing semantics.

Argumentation is based on the exchange and the evaluation of interacting arguments. Unlike Dung's theory where arguments are either accepted or rejected, ranking-based semantics rank-order arguments from the most to the least acceptable ones. We propose in this work six new ranking-based semantics. We argue that, contrarily to existing ranking semantics in the literature, that focus on evaluating attacks and defenses only, it is reasonable to give a prominent role to non-attacked arguments, as it is the case in standard Dung's semantics. Our six semantics are based on the propagation of the weight of each argument to its neighbors, where the weight of non-attacked arguments is greater than the attacked ones.

In this work we address the issue of uncertainty in abstract argumentation. We propose a way to compute the relative relevance of arguments by merging the classical argumentation framework proposed in [5] into a game theoretic coalitional setting, where the worth of a collection of arguments can be seen as the combination of the information concerning the defeat relation and the preferences over arguments of a “user”. Via a property-driven approach, we show that the Shapley value [17] for coalitional games defined over an argumentation framework, can be applied to resume all the information about the worth of sets of arguments into an attribution of relevance for the single arguments. We also prove that, for a large family of (coalitional) argumentation frameworks, the Shapley value can be easily computed.

In debates, agents are faced with the problem of deciding how to best contribute to the current state of the debate in order to satisfy their own goals. Target sets specify minimal changes on the current state of the debate that are required to achieve such goals, where changes are the addition and/or deletion of attacks among arguments. However, agents may not have the ability to implement all the actions prescribed by a target set, nor to rely on others to help them to do so. In this short paper we provide evidence that this notion is still a useful criterion for move selection.

Agents contributing to (online) debate systems often have different areas of expertise. This must be considered if we want to define a decision making process based on the output of such a system. Distinguishing agents on the basis of their areas of expertise also opens an interesting perspective: when a debate is deemed “controversial”, calling an additional expert may be a natural way to make the decision easier. We introduce possible definitions that capture these notions and we provide a preliminary analysis with the objective to help a designer find the “right” expert.

There already exist some links between argumentation and game theory. For instance, dynamic games can be used for simulating interactions between agents in an argumentation process. In this paper, we establish a new link between these domains in a static framework: we show how an argumentation framework can be translated into a CP-Boolean game and how this translation can be used for computing extensions of argumentation semantics. We give formal algorithms to do so.