{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:26Z","timestamp":1753893086043,"version":"3.41.2"},"reference-count":87,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned <jats:italic>a priori<\/jats:italic> 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 because 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 \u201ctreasure hunt\u201d, 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).<\/jats:p>","DOI":"10.3389\/frobt.2024.1384609","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T11:39:17Z","timestamp":1731411557000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Heuristic satisficing inferential decision making in human and robot active perception"],"prefix":"10.3389","volume":"11","author":[{"given":"Yucheng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Pingping","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Alers","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Egner","sequence":"additional","affiliation":[]},{"given":"Marc A.","family":"Sommer","sequence":"additional","affiliation":[]},{"given":"Silvia","family":"Ferrari","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/robotics12040093","article-title":"Classical and heuristic approaches for mobile robot path planning: a survey","volume":"12","author":"Abdulsaheb","year":"2023","journal-title":"Robotics"},{"key":"B2","first-page":"72","article-title":"Heuristics as decision-making habits of autonomous sensorimotor agents","author":"Batta","year":"2019"},{"volume-title":"Dynamic programming and optimal control: volume I","year":"2012","author":"Bertsekas","key":"B3"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45528-0","article-title":"Pattern recognition and machine learning","volume":"4","author":"Bishop","year":"2006","journal-title":"Springer"},{"key":"B5","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1093\/acprof:oso\/9780199216093.003.0009","article-title":"Bayesian brains and cognitive mechanisms: harmony or dissonance","volume-title":"The probabilistic mind: prospects for Bayesian cognitive science","author":"Brighton","year":"2008"},{"key":"B6","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1037\/0278-7393.29.4.611","article-title":"\u201cDecision making with the\u201d adaptive toolbox\u201d: influence of environmental structure, intelligence, and working memory load","volume":"29","author":"Br\u00f6der","year":"2003","journal-title":"J. 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