Date of Original Version



Conference Proceeding

Rights Management

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract or Description

The performance of a heterogeneous team depends critically on the composition of its members, and switching out one member for another can make a drastic difference. The capabilities of an agent depends not only on its individual characteristics, but also the interactions with its teammates. 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 and 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. Lastly, we describe an extension to our proposed model that captures mutual interactions between more than two agents





Published In

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011, 3638-3644.