Machine Reasoning for Effects-Based Operations: A Generic Architecture for Multi-Domain Workarounds Reasoning

"Effects-based operations must determine how the enemy might respond to air strikes. Current approaches to predicting enemy response to target damage suffer from serious limitations: they typically do not consider how the enemy might repair or modify thestructure of a target system, they typically reason only about a single type of target system, they cannot adequately represent delayed effects, concurrent actions, and uncertainty, and their models are difficult for analysts to construct and maintain.To address these deficiencies, we propose to develop innovative machine reasoning technology to predict enemy workarounds in target systems that are well modeled as networks. We exploit emerging knowledge acquisition technology to enable analysts toreadily build and maintain models of target systems and associated workarounds procedures. We also develop technology to automatically compile these models into a form amenable to efficient reasoning. Finally, we develop efficient algorithms to computeworkaround options and to predict enemy allocation of workaround resources.Phase I develops a suitable machine reasoning architecture and algorithms, and implements a prototype to validate the approach. Phase II scales this prototype to more target systems, demonstrates the benefits of analyzing multiple target systemssimultaneously, and extends the approach to accommodate uncertainty. The technology developed under this program will be of im