{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:50:33Z","timestamp":1759827033029,"version":"3.37.3"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2013 R1A2A2A01016589"],"award-info":[{"award-number":["2013 R1A2A2A01016589"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>The iterated prisoner\u2019s dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents\u2019 actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player\u2019s behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent\u2019s behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent\u2019s behavior than when the data were collected through random actions.<\/jats:p>","DOI":"10.1155\/2016\/7420984","type":"journal-article","created":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T18:10:35Z","timestamp":1455819035000},"page":"1-13","source":"Crossref","is-referenced-by-count":3,"title":["Active Player Modeling in the Iterated Prisoner\u2019s Dilemma"],"prefix":"10.1155","volume":"2016","author":[{"given":"Hyunsoo","family":"Park","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyung-Joong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2015.01.005"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2009.07.005"},{"first-page":"1","volume-title":"Game player modeling","year":"2016","key":"27"},{"year":"2007","key":"1"},{"year":"2011","key":"3"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1093\/imamat\/24.1.59"},{"year":"2010","key":"5"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2005.850293"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.2466\/pr0.2000.86.3c.1219"},{"key":"9","first-page":"83","volume":"176","year":"1994","journal-title":"Comparative Economic Studies"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1126\/science.7466396"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1038\/364056a0"},{"first-page":"32","volume-title":"The evolution of strategies in the iterated prisoner's dilemma","year":"1987","key":"11"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1993.1.1.77"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011657018442"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcc.2006.875423"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/tciaig.2011.2109718"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2011.2166268"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/tciaig.2015.2439061"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"22","first-page":"2825","volume":"12","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1177\/0022002795039001008"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/7420984.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/7420984.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/7420984.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,7,26]],"date-time":"2016-07-26T13:31:51Z","timestamp":1469539911000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2016\/7420984\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":23,"alternative-id":["7420984","7420984"],"URL":"https:\/\/doi.org\/10.1155\/2016\/7420984","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2016]]}}}