In distributed, heterogeneous, multi-agent teams, agents may have different capabilities and types of sensors.
Agents in dynamic environments will need to cooperate in real-time to perform tasks with minimal costs. Some
example scenarios include dynamic allocation of UAV and UGV robot teams to possible hurricane survivor
locations, search and rescue and target detection.
Auction based algorithms scale well because agents generally only need to communicate bid information. In
addition, the agents are able to perform their computations in parallel and can operate on local information.
Furthermore, it is easy to integrate humans and other vehicle types and sensor combinations into an auction
framework. However, standard auction mechanisms do not explicitly consider sensors with varying reliability.
The agents sensor qualities should be explicitly accounted. Consider a scenario with multiple agents, each
carrying a single sensor. The tasks in this case are to simply visit a location and detect a target. The sensors
are of varying quality, with some having a higher probability of target detection. The agents themselves may
have different capabilities, as well. The agents use knowledge of their environment to submit cost-based bids for
performing each task and an auction is used to perform the task allocation. This paper discusses techniques for
including a Bayesian formulation of target detection likelihood into this auction based framework for performing
task allocation across multi-agent heterogeneous teams. Analysis and results of experiments with multiple air
systems performing distributed target detection are also included.