An Experimental Study on Agent Learning for Market-based Sensor Management

Distributed Sensor management, the process of managing or coordinating the use of sensing resources in a distributed environment, is a multi-objective optimization problem. In our earlier work, we proposed MASM (Market-Architecture for Sensor Management), a market-based approach to allocate sensor resources in real-time to various resource requestors. MASM models the multi-objective sensor management problem as a combinatorial-auction based market where the network resources sell goods to the resource requestors. To allow the resource requestors to participate in the market, MASM grants "budgets" to these resource requestors based on their priority to the overall mission. However, for a given budget, self-interested resource requestors or buyers can learn from market-data and adapt their bidding behavior. This paper presents results of an initial experimental study, where the learning behavior of resource requestors is modeled and their effect on market performance is examined.