Artificial Intelligence - Special issue on Robocop: the first step
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
ISER '00 Experimental Robotics VII
Autonomous Agents that Learn to Better Coordinate
Autonomous Agents and Multi-Agent Systems
Making Allocations Collectively: Iterative Group Decision Making under Uncertainty
MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
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.