{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T20:14:27Z","timestamp":1764101667054,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1815886"],"award-info":[{"award-number":["IIS-1815886"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["UWSC12017-BPO49408"],"award-info":[{"award-number":["UWSC12017-BPO49408"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,8]]},"DOI":"10.1145\/3512290.3528829","type":"proceedings-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T20:02:57Z","timestamp":1657310577000},"page":"332-340","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fitness shaping for multiple teams"],"prefix":"10.1145","author":[{"given":"Joshua","family":"Cook","sequence":"first","affiliation":[{"name":"Oregon State University"}]},{"given":"Kagan","family":"Tumer","sequence":"additional","affiliation":[{"name":"Oregon State University"}]}],"member":"320","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1997.620107"},{"volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"Agogino A.","key":"e_1_3_2_1_2_1","unstructured":"A. Agogino, C. Holmes Parker, and K. Turner. 2012. Evolving Large Scale UAV Communication Systems. In Proceedings of the Genetic and Evolutionary Computation Conference. Philadelphia, PA."},{"key":"e_1_3_2_1_3_1","volume-title":"Proc. of AAMAS-05 Workshop on Coordination of Large Scale Multiagent Systems. Citeseer.","author":"Agogino Adrian","year":"2005","unstructured":"Adrian Agogino and Kagan Tumer. 2005. Reinforcement Learning in Large Multi-Agent Systems. In Proc. of AAMAS-05 Workshop on Coordination of Large Scale Multiagent Systems. Citeseer."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-008-9046-9"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9428"},{"key":"e_1_3_2_1_6_1","volume-title":"Learning Teammate Models For Ad Hoc Teamwork. In AAMAS Adaptive Learning Agents (ALA) Workshop. 57--63","author":"Barrett Samuel","year":"2012","unstructured":"Samuel Barrett, Peter Stone, Sarit Kraus, and Avi Rosenfeld. 2012. Learning Teammate Models For Ad Hoc Teamwork. In AAMAS Adaptive Learning Agents (ALA) Workshop. 57--63."},{"key":"e_1_3_2_1_7_1","unstructured":"Yu-Han Chang Tracey Ho and Leslie P Kaelbling. 2004. All Learning is Local: Multi-agent Learning in Global Reward Games. In Advances in neural information processing systems. 807--814."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6196"},{"key":"e_1_3_2_1_9_1","volume-title":"Athirai Aravazhi Irissappane, and Jie Zhang","author":"Chen Shuo","year":"2019","unstructured":"Shuo Chen, Ewa Andrejczuk, Athirai Aravazhi Irissappane, and Jie Zhang. 2019. ATSIS: Achieving the Ad Hoc Teamwork by Sub-task Inference and Selection. (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/375735.376030"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2936924.2937001"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Mitchell K Colby Sepideh Kharaghani Chris HolmesParker and Kagan Tumer. 2015. Counterfactual Exploration for Improving Multiagent Learning.. In AAMAS. 171--179.","DOI":"10.1007\/s10458-015-9318-0"},{"key":"e_1_3_2_1_13_1","first-page":"425","article-title":"Shaping Fitness Functions for Coevolving Cooperative Multiagent Systems","volume":"1","author":"Colby Mitchell K","year":"2012","unstructured":"Mitchell K Colby and Kagan Tumer. 2012. Shaping Fitness Functions for Coevolving Cooperative Multiagent Systems.. In AAMAS, Vol. 1. 425--432.","journal-title":"AAMAS"},{"key":"e_1_3_2_1_14_1","volume-title":"An Overview of Evolutionary Algorithms in Multiobjective optimization. Evolutionary computation 3, 1","author":"Fonseca Carlos M","year":"1995","unstructured":"Carlos M Fonseca and Peter J Fleming. 1995. An Overview of Evolutionary Algorithms in Multiobjective optimization. Evolutionary computation 3, 1 (1995), 1--16."},{"key":"e_1_3_2_1_15_1","volume-title":"2009 Fifth International Conference on Natural Computation","volume":"5","year":"2009","unstructured":"Ping-an Gao, Zi-xing Cai, and Ling-li Yu. 2009. Evolutionary Computation Approach to Decentralized Multi-Robot Task Allocation. In 2009 Fifth International Conference on Natural Computation, Vol. 5. IEEE, 415--419."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Katie Long Genter Noa Agmon and Peter Stone. 2011. Role-Based Ad Hoc Teamwork.. In Plan Activity and Intent Recognition. Citeseer 1782--1783.","DOI":"10.1609\/aaai.v25i1.8057"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding The Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9), Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 249--256. https:\/\/proceedings.mlr.press\/v9\/glorot10a.html"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45823-6_55"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/artl_a_00163"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45105-6_33"},{"key":"e_1_3_2_1_21_1","volume-title":"Genetic Algorithm-Based Multi-Robot Cooperative Exploration. In 2007 IEEE International Conference on Control and Automation. IEEE, 1018--1023","author":"Ma Xin","year":"2007","unstructured":"Xin Ma, Qin Zhang, and Yibin Li. 2007. Genetic Algorithm-Based Multi-Robot Cooperative Exploration. In 2007 IEEE International Conference on Control and Automation. IEEE, 1018--1023."},{"key":"e_1_3_2_1_22_1","first-page":"278","article-title":"Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping","volume":"99","author":"Ng Andrew Y","year":"1999","unstructured":"Andrew Y Ng, Daishi Harada, and Stuart Russell. 1999. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. In Icml, Vol. 99. 278--287.","journal-title":"Icml"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2012.67"},{"volume-title":"From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior","author":"\u00d8stergaard Esben H","key":"e_1_3_2_1_24_1","unstructured":"Esben H \u00d8stergaard, Henrik H Lund, and Reality Gap. 2002. Co-Evolving Robot Soccer Behavior. In From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior, Vol. 7. MIT Press, 351."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24854-5_59"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/645822.670374"},{"key":"e_1_3_2_1_27_1","volume-title":"Cooperative Coevolution: An Architecture For Evolving Coadapted Subcomponents. Evolutionary computation 8, 1","author":"Potter Mitchell A","year":"2000","unstructured":"Mitchell A Potter and Kenneth A De Jong. 2000. Cooperative Coevolution: An Architecture For Evolving Coadapted Subcomponents. Evolutionary computation 8, 1 (2000), 1--29."},{"key":"e_1_3_2_1_28_1","volume-title":"Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning. In International Conference on Machine Learning. PMLR, 8776--8786","author":"Rahman Muhammad A","year":"2021","unstructured":"Muhammad A Rahman, Niklas Hopner, Filippos Christianos, and Stefano V Albrecht. 2021. Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning. In International Conference on Machine Learning. PMLR, 8776--8786."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759651"},{"key":"e_1_3_2_1_30_1","unstructured":"Manish Chandra Reddy Ravula. 2019. Ad-hoc Teamwork with Behavior-switching Agents. Ph. D. Dissertation."},{"key":"e_1_3_2_1_31_1","volume-title":"Fitness Critics for Multiagent Learning. In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 222--224","author":"Rockefeller Golden","year":"2019","unstructured":"Golden Rockefeller, Patrick Mannion, and Kagan Tumer. 2019. Fitness Critics for Multiagent Learning. In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 222--224."},{"key":"e_1_3_2_1_32_1","volume-title":"Coevolution of Fitness Maximizers and Fitness Predictors. GECCO Late Breaking Paper","author":"Schmidt Michael","year":"2005","unstructured":"Michael Schmidt and Hod Lipson. 2005. Coevolution of Fitness Maximizers and Fitness Predictors. GECCO Late Breaking Paper (2005)."},{"key":"e_1_3_2_1_33_1","unstructured":"Reid Simmons David Apfelbaum Wolfram Burgard Dieter Fox Mark Moors Sebastian Thrun and H\u00e5kan Younes. 2000. Coordination for Multi-Robot Exploration and Mapping. In Aaai\/Iaai. 852--858."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Peter Stone Gal A Kaminka Sarit Kraus Jeffrey S Rosenschein et al. 2010. Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination.","DOI":"10.1609\/aaai.v24i1.7529"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICUAS.2019.8798078"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"K. Tumer and A. K. Agogino. 2007. Coordinating Multi-Rover Systems: Evaluation Functions for Dynamic and Noisy Environments. In Evolutionary Computation in Dynamic and Uncertain Environments S. Yang (Ed.). Springer 371--388.","DOI":"10.1007\/978-3-540-49774-5_16"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1402298.1402315"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v33i3.2426"},{"key":"e_1_3_2_1_39_1","volume-title":"Proc. of the Genetic and Evolutionary Computation Conference. Citeseer, 1406--1413","author":"Uchibe Eiji","year":"1999","unstructured":"Eiji Uchibe, Masateru Nakamura, and Minoru Asada. 1999. Cooperative and Competitive Behavior Acquisition for Mobile Robots Through Co-evolution. In Proc. of the Genetic and Evolutionary Computation Conference. Citeseer, 1406--1413."},{"key":"e_1_3_2_1_40_1","volume-title":"Self-Organized UAV Traffic in Realistic Environments. In 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, 1645--1652","author":"Vir\u00e1gh Csaba","year":"2016","unstructured":"Csaba Vir\u00e1gh, M\u00e1t\u00e9 Nagy, Carlos Gershenson, and G\u00e1bor V\u00e1s\u00e1rhelyi. 2016. Self-Organized UAV Traffic in Realistic Environments. In 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, 1645--1652."}],"event":{"name":"GECCO '22: Genetic and Evolutionary Computation Conference","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Boston Massachusetts","acronym":"GECCO '22"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512290.3528829","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512290.3528829","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512290.3528829","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:57Z","timestamp":1750183797000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512290.3528829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":40,"alternative-id":["10.1145\/3512290.3528829","10.1145\/3512290"],"URL":"https:\/\/doi.org\/10.1145\/3512290.3528829","relation":{},"subject":[],"published":{"date-parts":[[2022,7,8]]},"assertion":[{"value":"2022-07-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}