  Inferential decision-making algorithms typically assume that an underlying
probabilistic model of decision alternatives and outcomes may be learned a
priori or online. Furthermore, when applied to robots in real-world settings
they often perform unsatisfactorily or fail to accomplish the necessary tasks
because this assumption is violated and/or they experience unanticipated
external pressures and constraints. Cognitive studies presented in this and
other papers show that humans cope with complex and unknown settings by
modulating between near-optimal and satisficing solutions, including
heuristics, by leveraging information value of available environmental cues
that are possibly redundant. Using the benchmark inferential decision problem
known as ``treasure hunt", this paper develops a general approach for
investigating and modeling active perception solutions under pressure. By
simulating treasure hunt problems in virtual worlds, our approach learns
generalizable strategies from high performers that, when applied to robots,
allow them to modulate between optimal and heuristic solutions on the basis of
external pressures and probabilistic models, if and when available. The result
is a suite of active perception algorithms for camera-equipped robots that
outperform treasure-hunt solutions obtained via cell decomposition, information
roadmap, and information potential algorithms, in both high-fidelity numerical
simulations and physical experiments. The effectiveness of the new active
perception strategies is demonstrated under a broad range of unanticipated
conditions that cause existing algorithms to fail to complete the search for
treasures, such as unmodelled time constraints, resource constraints, and
adverse weather (fog).
