Multi-Hypothesis Contingency Driven Targeting

Period of Performance: 02/28/2008 - 08/28/2008

$99.5K

Phase 1 SBIR

Recipient Firm

Decisive Analytics Corp.
1400 Crystal Drive Array
Arlington, VA 22202
Principal Investigator

Abstract

Despite significant research, engagement management algorithms have not matured to the point of deployability - even the most advanced algorithms are still considered developmental. One reason for the absence of global engagement management algorithms in the deployed system is that providing good solutions to the weapon/target allocation problem is not sufficient. An automated engagement management tool must be able to process and respect the guidance contained in the pre-defined defense plan. Conversely, existing planning tools do little to acknowledge that their plans will be executed by automated engagement managed algorithms. The guidance and strategies contained within defense plans are not expressed in a form that is directly useable by engagement management algorithms. The DAC team proposes to close the gap between planning and execution through application of a novel class of algorithms that are able to rapidly identify near-optimal allocation schemes while remaining completely decoupled from their objective functions. By defining the defense plan s phases and contingencies in terms of different optimization constraints and objective functions, an engagement management algorithm will be able to automatically apply the guidance, policies and strategies defined by the plan to the execution phase of an engagement.