Using Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network

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dc.contributor.advisorGanesan, Ragesh
dc.contributor.authorStimpson, Daniel Edward
dc.creatorStimpson, Daniel Edward
dc.date.accessioned2018-10-22T01:21:18Z
dc.date.available2018-10-22T01:21:18Z
dc.date.issued2017
dc.description.abstractAmbushes, in the form of improvised explosive devices (IEDs), have posed grave risk to targeted vehicles operating on supply routes in recent theaters of war. History shows that this is an enduring problem that U.S. military forces will likely face again in the future. This research provides an underpinning argument and model demonstration of a previously unexplored approach to the attack prediction problem when conducting repetitive operations on a contested transportation network. The problem being addressed goes beyond the typical objective of maximizing IED detection and avoidance, or minimizing damage and delay. Rather the problem is re-framed to focus on using the defender's activities (that are being observed by the attacker) as a direct means to shape the attacker's expectations and therefore his attack choices. Thus, in contrast to most previous work, there is an explicit assumption of dependence between the defender's actions and the attacker's choices. Approximate dynamic programming (ADP) is applied in a reinforcement learning (RL) construct to determine convoy schedules and route clearance assignments in light of a responsive attacker. There are currently few analytical approaches for this problem in the literature, but RL algorithms offer opportunities for meaningful improvements by optimizing individual movements across an extended planning horizon, accounting for downstream attacker-defender interaction. Computational results show meaningful performance improvements over a one-step, myopic decision rule. Further, the decision policies that are discovered by the RL agent would be difficult for unaided human planners to duplicate.
dc.format.extent148 pages
dc.identifier.urihttps://hdl.handle.net/1920/11314
dc.language.isoen
dc.rightsCopyright 2017 Daniel Edward Stimpson
dc.subjectOperations research
dc.subjectMilitary studies
dc.subjectTransportation
dc.subjectAttacker-Defender
dc.subjectDynamic programming
dc.subjectImprovised Explosive Device
dc.subjectReinforcement Learning
dc.subjectRoute Clearance
dc.subjectVehicle Routing
dc.titleUsing Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network
dc.typeDissertation
thesis.degree.disciplineSystems Engineering and Operations Research
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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