Exploiting Domain Knowledge in Collaborative Missions

In collaborative missions, coalition members depend on one another to deliver on different aspects of shared goals. In such settings, task delegation decisions are complicated because activities of coalition members may be regulated by policies. Especially when such policies are private, learning these policies become crucial to estimate the outcome of delegation decisions. In this paper, we present an approach that utilises domain knowledge in aiding the learning of policies. Our approach combines ontological reasoning, machine learning and argumentation in a novel way to accomplish this. Using our approach, decision makers can reason about the policies that others are operating with, and make informed decisions about to whom to delegate a task. In a series of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that more accurate models of others’ policies can be developed more rapidly using various forms of domain knowledge.