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Feigh, Karen M.

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    Judicial 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.
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    Examination of Human Performance During Lunar Landing
    (Georgia Institute of Technology, 2010-03) Chua, Zarrin K. ; Feigh, Karen M. ; Braun, Robert D.
    Experimentally derived data was extrapolated to compare the lunar landing performance of human pilots to that of an automated landing system.12 The results of this investigation are presented. Overall, the pilots performed equal to or better than the automated system in 18% of the relevant cases, but required more fuel. Pilot site selections were further investigated as a function of the time to complete. Each hypothetical case was compared to the automated system, across a range of performance criteria weighting distributions. This performance criteria is threefold – proximity to point of interest, safety of the site, and fuel consumed. In general, the pilots perform better than the automated system in terms of safety and proximity to points of interest criteria. However, as the priority of fuel conservation increases, the tradeoff between using an autonomous landing system versus a human-in-command system favors the automation, especially if the pilot is not able to make the proper decision within a performance criteria specific threshold.
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    Modeling Cockpit Interface Usage During Lunar Landing Redesignation
    (Georgia Institute of Technology, 2009-04) Chua, Zarrin K. ; Major, Laura M. ; Feigh, Karen M.
    Fulfilling NASA’s space exploration objectives requires precision landing to reach lunar sites of interest. During the approach and landing stages, a landing point redesignation (LPR) display will provide information to the crew regarding the characteristics of alternate touchdown points. Building on a previous study which examined crew tasks during LPR but did not account for the specialized behavior of experts, this investigation will present a new task sequence model, specific to expert decision-making. This analysis furthers the development of a predictive task execution model, which is used to test the efficacy of alternate information display and operator actuator design concepts. The task model and cockpit display recommendations presented in this study provide a significant improvement in LPR task execution time. This paper examines the task sequence during lunar landing, describes the predictive task execution process model, and recommends cockpit display requirements for effective decision making.