Evidence gathering for hypothesis resolution using judicial evidential reasoning

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 rigorous framework for gathering evidence to resolve hypotheses notwithstanding ambiguous, incomplete, and uncertain evidence. Studies have shown that decision-makers demonstrate several biases in decisions involving probability judgment, so decision-makers must be confident that the evidence-based hypothesis resolution is strong and impartial before declaring a resolution. The proposed Judicial Evidential Reasoning framework encodes decision-maker questions as rigorously testable hypotheses to be interrogated through evidence-gathering actions. Dempster-Shafer theory is applied to model hypothesis knowledge and quantify ambiguity, and an equal-effort heuristic is proposed to balance time-efficiency and impartiality. Adversarial optimization techniques are used to make many-hypothesis resolution computationally tractable. This work includes derivation of the generalized formulation, computational tractability considerations for improved performance, several illustrative examples, and application to a space situational awareness sensor network tasking scenario. The results show strong hypothesis resolution and robustness to fixation due to poor prior evidence.