ABSTRACT
This chapter reviews two of the most promising techniques for nonmyopic sensor management, stochastic dynamic programming, and market-oriented programming. It introduces the stochastic dynamic programming approach and focuses on a few crucial aspects of this approach. The chapter examines market-oriented programming, and presents an example implementation and some concluding remarks. Stochastic dynamic programming approaches have been applied to sensor management problems. Market-oriented programming techniques use market algorithms for resource allocation in distributed environments. The chapter provides a multisensor, multiconsumer, multitarget platform to serve as a test bed for market architecture for sensor management. The search area has several different kinds of sensors, including sensors that provide range and bearing, bearings only sensors, electronic support measure sensors. The simulation has a set of software agents that search for and destroy targets. Approximate dynamic programming approaches offer a rigorous framework for this problem but require elaborate problem-specific formulation.
