Abstract
This paper derives the structure of optimal sequential decision algorithms for interference from severely uncertain evidence. Uncertainty is represented with convex models of uncertainty. We formulate the decision problem as a selection between competing hypotheses. We define the optimality of decision in terms of the robustness of the algorithm to the associated uncertainties. Binary,N-ary and sequential decisions are studied. Examples are discussed which deal with tracking an evasive target, and with selecting features for pattern recognition.
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