{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T09:54:21Z","timestamp":1756461261947,"version":"3.41.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T00:00:00Z","timestamp":1616803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"US Office of Naval Research Awards","award":["N000141210987, N00014161302, N000141613025"],"award-info":[{"award-number":["N000141210987, N00014161302, N000141613025"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Hum.-Robot Interact."],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>\n            Robotic collectives are composed of hundreds or thousands of distributed robots using local sensing and communication that encompass characteristics of biological spatial swarms, colonies, or a combination of both. Interactions between the individual entities can result in emergent collective behaviors. Human operators in future disaster response or military engagement scenarios are likely to deploy semi-autonomous collectives to gather information and execute tasks within a wide area, while reducing the exposure of personnel to danger. This article presents and evaluates two action selection models in an experiment consisting of a single human operator supervising four simulated collectives. The action selection models have two parts: (1) a best-of-\n            <jats:italic>n<\/jats:italic>\n            decision-making model that attempts to choose the highest-quality target from a set of\n            <jats:italic>n<\/jats:italic>\n            targets and (2) a quorum sensing task sequencing model that enables autonomous target site occupation. An original biologically inspired insect colony decision model is compared to a bias-reducing model that attempts to reduce environmental bias, which can negatively influence collective best-of-\n            <jats:italic>n<\/jats:italic>\n            decisions when poorer-quality targets are easier to evaluate than higher-quality targets. The collective decision-making models are compared in both supervised and unsupervised trials. The bias-reducing model without human supervision is slower than the original model but is 57% more accurate for decisions where evaluating the optimal target is more difficult. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions compared to the teams using the original model.\n          <\/jats:p>","DOI":"10.1145\/3442679","type":"journal-article","created":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T10:17:28Z","timestamp":1616840248000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Human-Collective Collaborative Target Selection"],"prefix":"10.1145","volume":"10","author":[{"given":"Jason R.","family":"Cody","sequence":"first","affiliation":[{"name":"United States Military Academy, West Point, NY"}]},{"given":"Karina A.","family":"Roundtree","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis OR"}]},{"given":"Julie A.","family":"Adams","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis OR"}]}],"member":"320","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"volume-title":"Companion of the 2018 ACM\/IEEE International Conference on Human-Robot Interaction (HRI\u201918)","author":"Adams Julie A.","key":"e_1_2_1_1_1","unstructured":"Julie A. 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In Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC\u201919) . 4275--4282. C. C. Ashcraft, M. A. Goodrich, and J. W. Crandall. 2019. Moderating operator influence in human-swarm systems. In Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC\u201919). 4275--4282."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-012-0075-2"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5898\/JHRI.5.1.Brown"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0019888"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.00053"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2008.0014"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS\u201917)","author":"Jason","year":"2017","unstructured":"Jason R. Cody and Julie A. Adams. 2017. An evaluation of quorum sensing mechanisms in collective value-sensitive site selection . In Proceedings of the 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS\u201917) . 40--47. DOI:https:\/\/doi.org\/10.1109\/MRS. 2017 .8250929 Jason R. Cody and Julie A. Adams. 2017. An evaluation of quorum sensing mechanisms in collective value-sensitive site selection. In Proceedings of the 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS\u201917). 40--47. DOI:https:\/\/doi.org\/10.1109\/MRS.2017.8250929"},{"volume-title":"Proceedings of the International Symposium on Swarm Behavior and Bio-Inspired Robots. 223--230","author":"Jason","key":"e_1_2_1_11_1","unstructured":"Jason R. Cody and Julie A. Adams. 2017. Reducing distance bias in value-sensitive swarm site selection . In Proceedings of the International Symposium on Swarm Behavior and Bio-Inspired Robots. 223--230 . Jason R. Cody and Julie A. Adams. 2017. 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Crandall, Nathan Anderson, Chace Ashcraft, John Grosh, Jonah Henderson, Joshua McClellan, Aadesh Neupane, and Michael A. Goodrich. 2017. Human-swarm interaction as shared control: Achieving flexible fault-tolerant systems. In Engineering Psychology and Cognitive Ergonomics: Performance, Emotion and Situation Awareness, Don Harris (Ed.). Springer International, 266--284. DOI:https:\/\/doi.org\/10.1007\/978-3-319-58472-0_21"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1518\/001872095779049543"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2002.1066"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1177\/1059712309103430"},{"volume-title":"Proceedings of the International Workshop on Combinations of Intelligent Methods and Applications. 29--37","author":"Haque Musad","key":"e_1_2_1_19_1","unstructured":"Musad Haque , Christopher Ren , Electa Baker , Douglas Kirkpatrick , and Julie A. Adams . 2016. 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