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 efficient processes for prioritizing between candidate action sequences. The proposed Judicial Evidential Reasoning framework encodes decision-maker questions as rigorously testable hypotheses and proposes actions to resolve the hypotheses in the face of ambiguous, incomplete, and uncertain evidence. Dempster-Shafer theory is applied to model hypothesis knowledge and quantify ambiguity, and an equal-effort heuristic is proposed time-efficiency and impartiality to combat confirmation bias. This work includes derivation of the generalized formulation, computational tractability considerations for improved performance, several illustrative examples, and sample 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.

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