{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:51:23Z","timestamp":1768557083816,"version":"3.49.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"3-4","license":[{"start":{"date-parts":[[2004,12,1]],"date-time":"2004-12-01T00:00:00Z","timestamp":1101859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2004,12]]},"abstract":"<jats:p> This paper addresses qualitative and quantitative diversity and specialization issues in the framework of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm\u2019s heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters. <\/jats:p>","DOI":"10.1177\/105971230401200306","type":"journal-article","created":{"date-parts":[[2004,12,14]],"date-time":"2004-12-14T11:22:57Z","timestamp":1103023377000},"page":"199-212","source":"Crossref","is-referenced-by-count":41,"title":["Learning and Measuring Specialization in Collaborative Swarm Systems"],"prefix":"10.1177","volume":"12","author":[{"given":"Ling","family":"Li","sequence":"first","affiliation":[{"name":"Learning Systems Group, California Institute of Technology,"}]},{"given":"Alcherio","family":"Martinoli","sequence":"additional","affiliation":[{"name":"Swarm-Intelligent Systems Research Group, EPFL,"}]},{"given":"Yaser S.","family":"Abu-Mostafa","sequence":"additional","affiliation":[{"name":"Learning Systems Group, California Institute of Technology,"}]}],"member":"179","published-online":{"date-parts":[[2004,12,1]]},"reference":[{"key":"atypb1","first-page":"1090","volume-title":"Proceedings of the first international joint conference on autonomous agents and multiagent systems","author":"Agassounon, W."},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1201\/9781439863671-10"},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1201\/9781439863671-8"},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195131581.001.0001"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1006\/jtbi.2001.2506"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574703004946"},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011227210047"},{"key":"atypb8","volume-title":"Mathematical taxonomy","author":"Jardine, N.","year":"1971"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(99)00066-4"},{"key":"atypb10","first-page":"32","volume-title":"Proceedings of the first international workshop on biologically inspired approaches to advanced information technology","author":"Labella, T. H."},{"key":"atypb11","unstructured":"Li, L. (2002). Distributed learning in swarm systems: A case study.                 Unpublished master\u2019s thesis, California Institute of Technology, Pasadena, CA."},{"key":"atypb12","first-page":"261","volume-title":"Lecture notes in computer science: Vol. 2412. Intelligent data engineering and automated learning\u2014IDEAL 2002","author":"Li, L."},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1177\/0278364904042197"},{"key":"atypb14","first-page":"3","volume-title":"Lecture notes in control and information sciences: Vol. 223. Experimental robotics IV","author":"Martinoli, A."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1080\/095281398146806"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1007\/s004220050351"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1007\/BF00164007"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1007\/978-4-431-67919-6_37"},{"key":"atypb19","first-page":"40","volume-title":"Lecture notes in artificial intelligence: Vol. 1221. 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