Arbitrage Look-ahead Agents for Market-based Optimization (ALAMO)

Missile defense takes place in an unpredictable, real-time environment and thus requires an adaptive approach to optimization that dynamically allocates sensors and their supporting resources in response to changing goals and constraints. Here, we propose a system of Arbitrage Look-ahead Agents for Market-based Optimization (ALAMO) to meet the challenge of this real-time resource allocation problem. This approach applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents’ bidding strategies are based on game theory, and they are designed so that the aggregate result of the process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. We extend market-based optimization using arbitrage look-ahead agents to turn this approach into a non-myopic planning algorithm, which provides computational gains, more efficient allocations of sensor tasks, and risk management framework that hedges against uncertainty.