{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:10:41Z","timestamp":1777522241449,"version":"3.51.4"},"reference-count":56,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2016,12,1]],"date-time":"2016-12-01T00:00:00Z","timestamp":1480550400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p>This paper investigates the performance of synthetic agents in playing and learning scenarios in a turn-based zero-sum game and highlights the ability of opponent-based learning models to demonstrate competitive playing performances in social environments. Synthetic agents are generated based on a variety of combinations of some key parameters, such as exploitation-vs-exploration trade-off, learning back-up and discount rates, and speed of learning, and interact over a very large number of games on a grid infrastructure; experimental data is then analysed to generate clusters of agents that demonstrate interesting associations between eventual performance ranking and learning parameters\u2019 set-up. The evolution of these clusters indicates that agents with a predisposition to knowledge exploration and slower learning tend to perform better than exploiters, which tend to prefer fast learning. Observing these clusters vis-\u00e0-vis the playing behaviours of the agents makes it also possible to investigate how to select opponents best from a group; initial results suggest that good progress and stable evolution arise when an agent faces opponents of increasing capacity, and that an agent with a good learning mechanism set-up progresses better when it faces less favourably set-up agents.<\/jats:p>","DOI":"10.1177\/1059712316679239","type":"journal-article","created":{"date-parts":[[2016,12,9]],"date-time":"2016-12-09T02:40:15Z","timestamp":1481251215000},"page":"411-427","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Synthetic learning agents in game-playing social environments"],"prefix":"10.1177","volume":"24","author":[{"given":"Chairi","family":"Kiourt","sequence":"first","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, Patra, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Kalles","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, Patra, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,12,8]]},"reference":[{"key":"bibr1-1059712316679239","volume-title":"Developments in E-systems Engineering","author":"Al-Khateeb B.","year":"2011"},{"key":"bibr2-1059712316679239","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(78)90683-6"},{"key":"bibr3-1059712316679239","first-page":"493","volume-title":"Proceedings of 15th National Conference of the American Association on Artificial Intelligence","author":"Billings D.","year":"1998"},{"key":"bibr4-1059712316679239","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2016.03.005"},{"key":"bibr5-1059712316679239","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2007.913919"},{"key":"bibr6-1059712316679239","doi-asserted-by":"crossref","unstructured":"Caballero A., Botia J., Gomez-Skarmeta A. 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