(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.