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Andrew K. Hull
(Advisor: Prof. Mavris)
will defend a doctoral thesis entitled,
A Methodology for Technology-tuned Decision Behavior Algorithms for Tactics Exploration
On
Thursday, August 12 at 1:00 p.m.
https://bluejeans.com/833713414/9998
Abstract
In 2016, the USAF found that current development and acquisition methods may be inadequate to achieve air superiority in 2030. The airspace is expected to be highly contested by 2030 due to the Anti-Access/Area Denial strategies being employed by adversaries. Capability gaps must be addressed in order to maintain air superiority. The USAF identified new development and acquisition paradigms as the number one non-material capability development area. The idea of a new development and acquisition paradigm is not new. Such a paradigm shift occurred during the transition from threat-based acquisition during the cold war to capability-based acquisition during the war on terror.
Investigation into current US development and acquisition methods found several notional methodologies. Effectiveness-Based Design and Technology Identification, Evaluation, and Selection for Systems-of-Systems have been proposed as notional solutions. Both methodologies seek to evaluate the means – the technologies used to perform a mission – and the ways – the tactics used to complete a mission – of the technology design space. Proper evaluation of the ways would provide critical information to the decision-maker during technology selection. These findings suggest that a new paradigm focused on effectiveness-based acquisition is needed to improve current development and acquisition methods. To evaluate the ways design space, current methods must move away from a fixed or constrained mission model to one that is minimally defined and capable of exploring tactics for each unique technology.
The proposed Technology-tuned Decision Behavior Algorithms for Tactics Exploration (Tech-DEBATE) methodology enables the exploration of the ways, or more formally, the mission action design space. The methodology enables further exploration of the technology design space by improving the quantification of mission effectiveness through deep reinforcement learning. The data's foundation is based on traceable tactical alternatives that increase the confidence in the measures of effectiveness for each technology-tactic alternative. The methodology enables more informed decisions for technology investment, thereby reducing risks in the development and acquisition of new technologies. The reduction in risk inherently reduces the costs and development time associated with investment in new technologies. The Tech-DEBATE methodology provides a new methodology for technology evaluation through its emphasis on quantifying mission effectiveness in a minimally defined mission to inform technology investment decisions.
Committee
