Mutual state-based capabilities for role assignment in heterogeneous teams

In a heterogeneous team, agents have different capabilities with regards to the actions relevant to the task. Roles are typically assigned to individual agents in such a team, where each role is responsible for a certain aspect of the joint team goal. In this paper, we focus on role assignment in a heterogeneous team, where an agent's capability depends on its teammate and their mutual state, i.e., the agent's state and its teammate's state. The capabilities of an agent are represented by a mean and variance, to capture the uncertainty in the agent's actions as well as the uncertainty in the world. We present a formal framework for representing this problem, and illustrate our framework using a robot soccer example. We formally describe how to compute the value of a role assignment policy, as well as the computation of the optimal role assignment policy, using a notion of risk. Further, we show that finding the optimal role assignment can be difficult, and describe approximation algorithms that can be used to solve this problem. We provide an analysis of these algorithms in our model and empirically show that they perform well in general problems of this domain, compared to market-based techniques.