Coalition calculation in a dynamic agent environment

We consider a dynamic market-place of self-interested agents with differing capabilities. A task to be completed is proposed to the agent population. An agent attempts to form a coalition of agents to perform the task. Before proposing a coalition, the agent must determine the optimal set of agents with whom to enter into a coalition for this task; we refer to this activity as coalition calculation. To determine the optimal coalition, the agent must have a means of calculating the value of any given coalition. Multiple metrics (cost, time, quality etc.) determine the true value of a coalition. However, because of conflicting metrics, differing metric importance and the tendency of metric importance to vary over time, it is difficult to obtain a true valuation of a given coalition. Previous work has not addressed these issues. We present a solution based on the adaptation of a multi-objective optimization evolutionary algorithm. In order to obtain a true valuation of any coalition, we use the concept of Pareto dominance coupled with a distance weighting algorithm. We determine the Pareto optimal set of coalitions and then use an instance-based learning algorithm to select the optimal coalition. We show through empirical evaluation that the proposed technique is capable of eliciting metric importance and adapting to metric variation over time.