A comparison of three information gathering strategies in DAI systems under noisy conditions

This paper investigates a problem of task allocation in Distributed Artificial Intelligence (DAI) systems, where a coordinator allocates tasks among multiple agents in an optimal manner. In making the task allocation, the coordinator needs to understand the preference orders of the agents for the different task bundles. The coordinator does this by adopting an information acquisition strategy that leads to an optimal system welfare. Three different information acquisition strategies are investigated here. The strategies are compared in a noisy environment for the quality of information they provide in terms of the deviation from optimal system welfare.