A team of robotic agents for surveillance
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
The k-traveling repairman problem
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Legion System: A Novel Approach to Evolving Hetrogeneity for Collective Problem Solving
Proceedings of the European Conference on Genetic Programming
MURDOCH: Publish/Subscribe Task Allocation for Heterogeneous Agents
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An auction-based approach to complex task allocation for multirobot teams
An auction-based approach to complex task allocation for multirobot teams
The power of sequential single-item auctions for agent coordination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multi-robot coalition formation in real-time scenarios
Robotics and Autonomous Systems
| Hi-index | 0.00 |
Many realistic problem domains are composed of heterogeneous tasks distributed in a physical environment. A team of mobile agents has to autonomously allocate these tasks, navigate to them and finally execute them. Recently auctions have been used for task allocation among homogeneous agents. Less studied is the case of allocation where both the tasks and the agents are heterogeneous in nature. In this paper, we investigate the market-based allocation of heterogeneous tasks to heterogeneous agents in domains where the distribution of the task heterogeneity is known a priori. We present a model of task heterogeneity, and define a metric that allows us to assess the fitness of a team for a particular task domain. We then present a sequential, round-based, auction setup for allocating heterogeneous tasks to heterogeneous teams and empirically investigate the performance of three different allocation strategies.