Coalitions of Arguments: An Approach with Constraint Programming

The aggregation of generic items into coalitions leads to the creation of sets of homogenous entities. In this paper we accomplish this for an input set of arguments, and the result is a partition according to distinct lines of thought, i.e., groups of "coherent" ideas. We extend Dung\u27s Argumentation Framework (AF) in order to deal with coalitions of arguments. The initial set of arguments is partitioned into not-intersected subsets. All the found coalitions show the same property inherited by Dung, e.g., all the coalitions in the partition are admissible (or conflict-free, complete, stable): they are generated according to Dung\u27s principles. Each of these coalitions can be assigned to a different agent. We use Soft Constraint Programming as a formal approach to model and solve such partitions in weighted AFs: semiring algebraic structures can be used to model different optimization criteria for the obtained coalitions. Moreover, we implement and solve the presented problem with JaCoP, a Java constraint solver, and we test the code over a small-world network

Decentralised Coalition Formation Methods for Multi-Agent Systems

Coalition formation is a process whereby agents recognise that cooperation with others can occur in a mutually beneficial manner and therefore the agents can choose appropriate temporary groups (named coalitions) to form. The benefit of each coalition can be measured by: the goals it achieves; the tasks it completes; or the utility it gains. Determining the set of coalitions that should form is difficult even in centralised cooperative circumstances due to: (a) the exponential number of different possible coalitions; (b) the ``super exponential'' number of possible sets of coalitions; and (c) the many ways in which the agents of a coalition can agree to distribute its gains between its members (if this gain can be transferred between the agents). The inherent distributed and potentially self-interested nature of multi-agent systems further complicates the coalition formation process. How to design decentralised coalition formation methods for multi-agent systems is a significant challenge and is the topic of this thesis. The desirable characteristics for these methods to have are (among others): (i) a balanced computational load between the agents; (ii) an optimal solution found with distributed knowledge; (iii) bounded communication costs; and (iv) to allow coalitions to form even when the agents disagree on their values. The coalition formation methods presented in this thesis implement one or more of these desirable characteristics. The contribution of this thesis begins with a decentralised dialogue game that utilise argumentation to allow agents to reason over and come to a conclusion on what are the best coalitions to form, when the coalitions are valued qualitatively. Next, the thesis details two decentralised algorithms that allow the agents to complete the coalition formation process in a specific coalition formation model, named characteristic function games. The first algorithm allows the coalition value calculations to be distributed between the agents of the system in an approximately equal manner using no communication, where each agent assigned to calculate the value of a coalition is included in that coalition as a member. The second algorithm allows the agents to find one of the most stable coalition formation solutions, even though each agent has only partial knowledge of the system. The final contribution of this thesis is a new coalition formation model, which allows the agents to find the expected payoff maximising coalitions to form, when each agent may disagree on the quantitative value of each coalition. This new model introduces more risk to agents valuing a coalition higher than the other agents, and so encourages pessimistic valuations