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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemJudicial Evidential Reasoning for Decision Support Applied to Orbit Insertion Failure(Georgia Institute of Technology, 2017-11) Jaunzemis, Andris D. ; Minotra, Dev ; Holzinger, Marcus J. ; Feigh, Karen M. ; Chan, Moses W. ; Shenoy, Prakash P.Realistic decision-making often occurs with insufficient time to gather all possible evidence before a decision must be rendered, requiring an efficient process for prioritizing between potential action sequences. This work aims to develop a decision support system for tasking sensor networks to gather evidence to resolve hypotheses in the face of ambiguous, incomplete, and uncertain evidence. Studies have shown that decision-makers demonstrate several biases in decisions involving probability judgement, so decision-makers must be confident that the evidence-based hypothesis resolution is strong and impartial before declaring an anomaly or reacting to a conjunction analysis. Providing decision-makers with the ability to estimate uncertainty and ambiguity in knowledge has been shown to augment effectiveness. The proposed framework, judicial evidential reasoning (JER), frames decision-maker questions as rigorously testable hypotheses and employs an alternating-agent minimax optimization on belief in the null proposition. This approach values impartiality in addition to time efficiency: an ideal action sequence gathers evidence to quickly resolve hypotheses while guarding against bias. JER applies the Dempster-Shafer theory of belief functions to model knowledge about hypotheses and quantify ambiguity, and adversarial optimization techniques are used to make many-hypothesis resolution computationally tractable. This work includes derivation and application of the JER formulation to a GTO insertion maneuver anomaly scenario.