{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:58:12Z","timestamp":1774627092823,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T00:00:00Z","timestamp":1629504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S011609\/1"],"award-info":[{"award-number":["EP\/S011609\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Modelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent\u2019s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents\u2019 behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support.<\/jats:p>","DOI":"10.1007\/s12293-021-00343-8","type":"journal-article","created":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T06:02:26Z","timestamp":1629525746000},"page":"19-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Modelling other agents through evolutionary behaviours"],"prefix":"10.1007","volume":"14","author":[{"given":"Yifeng","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Ran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biyang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,21]]},"reference":[{"key":"343_CR1","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.artint.2018.01.002","volume":"258","author":"SV Albrecht","year":"2018","unstructured":"Albrecht SV, Stone P (2018) Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif Intell 258:66\u201395","journal-title":"Artif Intell"},{"key":"343_CR2","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.ins.2020.06.010","volume":"537","author":"P Andersen","year":"2020","unstructured":"Andersen P, Goodwin M, Granmo O (2020) Towards safe reinforcement-learning in industrial grid-warehousing. Inf Sci 537:467\u2013484","journal-title":"Inf Sci"},{"key":"343_CR3","doi-asserted-by":"crossref","unstructured":"Barrett S, Stone P, Kraus S, Rosenfeld A (2013) Teamwork with limited knowledge of teammates. In: Proceedings of the twenty-seventh AAAI conference on artificial intelligence. AAAI Press, pp 102\u2013108","DOI":"10.1609\/aaai.v27i1.8659"},{"key":"343_CR4","unstructured":"Carmel D, Markovitch S (1996) Learning models of intelligent agents. In: Proceedings of the thirteenth national conference on artificial intelligence\u2014vol 1. AAAI Press, pp 62\u201367"},{"key":"343_CR5","unstructured":"Conroy R, Zeng Y, Cavazza M, Tang J, Pan Y (2016) A value equivalence approach for solving interactive dynamic influence diagrams. In: Proceedings of the 15th international conference on autonomous agents and multiagent systems (AAMAS), pp 1162\u20131170"},{"issue":"3","key":"343_CR6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s10458-008-9064-7","volume":"18","author":"P Doshi","year":"2009","unstructured":"Doshi P, Zeng Y, Chen Q (2009) Graphical models for interactive pomdps: representations and solutions. J Auton Agents Multi-Agent Syst (JAAMAS) 18(3):376\u2013416","journal-title":"J Auton Agents Multi-Agent Syst (JAAMAS)"},{"issue":"1","key":"343_CR7","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10458-012-9204-y","volume":"27","author":"B Eker","year":"2013","unstructured":"Eker B, Akin L (2013) Solving decentralized POMDP problems using genetic algorithms. Auton Agents Multi Agent Syst 27(1):161\u2013196","journal-title":"Auton Agents Multi Agent Syst"},{"issue":"1","key":"343_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/S0899-8256(03)00025-3","volume":"45","author":"DP Foster","year":"2003","unstructured":"Foster DP, Young H (2003) Learning, hypothesis testing, and Nash equilibrium. Games Econ Behav 45(1):73\u201396","journal-title":"Games Econ Behav"},{"key":"343_CR9","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1613\/jair.1579","volume":"24","author":"PJ Gmytrasiewicz","year":"2005","unstructured":"Gmytrasiewicz PJ, Doshi P (2005) A framework for sequential planning in multiagent settings. J Artif Intell Res (JAIR) 24:49\u201379","journal-title":"J Artif Intell Res (JAIR)"},{"key":"343_CR10","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.procs.2016.05.374","volume":"80","author":"W Korczynski","year":"2016","unstructured":"Korczynski W, Byrski A, Kisiel-Dorohinicki M (2016) Efficient memetic continuous optimization in agent-based computing. Procedia Comput Sci 80:845\u2013854","journal-title":"Procedia Comput Sci"},{"key":"343_CR11","doi-asserted-by":"crossref","unstructured":"Mau\u00e1 DD, de\u00a0Campos CP, Zaffalon M (2011) Solving limited memory influence diagrams. CoRR arXiv:1109.1754","DOI":"10.1613\/jair.3625"},{"key":"343_CR12","unstructured":"Pynadath DV, Marsella S (2007) Minimal mental models. In: Proceedings of the twenty-second AAAI conference on artificial intelligence. AAAI Press, pp 1038\u20131044"},{"key":"343_CR13","unstructured":"Racani\u00e8re S, Weber T, Reichert D.P, Buesing L, Guez A, Rezende D.J, Badia A.P, Vinyals O, Heess N, Li Y, Pascanu R, Battaglia P, Hassabis D, Silver D, Wierstra D (2017) Imagination-augmented agents for deep reinforcement learning. In: NIPS, pp 5694\u20135705"},{"key":"343_CR14","doi-asserted-by":"crossref","unstructured":"Rathnasabapathy B, Doshi P, Gmytrasiewicz P.J (2006) Exact solutions of interactive pomdps using behavioral equivalence. In: The fifth international joint conference on autonomous agents and multiagent systems. ACM, pp 1025\u20131032","DOI":"10.1145\/1160633.1160816"},{"issue":"2","key":"343_CR15","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s10458-007-9026-5","volume":"17","author":"S Seuken","year":"2008","unstructured":"Seuken S, Zilberstein S (2008) Formal models and algorithms for decentralized decision making under uncertainty. J Autonom Agents Multi-Agent Syst 17(2):190\u2013250","journal-title":"J Autonom Agents Multi-Agent Syst"},{"issue":"7587","key":"343_CR16","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484\u2013489","journal-title":"Nature"},{"key":"343_CR17","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-7091-2490-1_22","volume-title":"User modeling","author":"D Suryadi","year":"1999","unstructured":"Suryadi D, Gmytrasiewicz PJ (1999) Learning models of other agents using influence diagrams. In: Kay J (ed) User modeling. Springer, Vienna, pp 223\u2013232"},{"issue":"2","key":"343_CR18","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/21.52548","volume":"20","author":"JA Tatman","year":"1990","unstructured":"Tatman JA, Shachter RD (1990) Dynamic programming and influence diagrams. IEEE Trans Syst Man Cybern 20(2):365\u2013379","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"343_CR19","unstructured":"V A.S, Peter S (2017) Reasoning about hypothetical agent behaviours and their parameters. In: Proceedings of the 16th conference on autonomous agents and multiagent systems, pp 547\u2013555"},{"key":"343_CR20","unstructured":"Wells C, Lusena C, Goldsmith J (1999) Genetic algorithms for approximating solutions to pomdps. In: Proceedings of the American Association for Artificial Intelligence\u00a0(AAAI), pp 1\u20138"},{"key":"343_CR21","volume-title":"Genetic programming: an introduction: on the automatic evolution of computer programs and its applications","author":"B Wolfgang","year":"1998","unstructured":"Wolfgang B, Frank F, Robert K, Peter N (1998) Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco"},{"issue":"1","key":"343_CR22","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TEVC.2016.2577593","volume":"21","author":"Y Zeng","year":"2017","unstructured":"Zeng Y, Chen X, Ong Y, Tang J, Xiang Y (2017) Structured memetic automation for online human-like social behavior learning. IEEE Trans Evol Comput 21(1):102\u2013115","journal-title":"IEEE Trans Evol Comput"},{"key":"343_CR23","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1613\/jair.3461","volume":"43","author":"Y Zeng","year":"2012","unstructured":"Zeng Y, Doshi P (2012) Exploiting model equivalences for solving interactive dynamic influence diagrams. J Artif Intell Res (JAIR) 43:211\u2013255","journal-title":"J Artif Intell Res (JAIR)"},{"issue":"2","key":"343_CR24","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s10115-015-0912-x","volume":"49","author":"Y Zeng","year":"2016","unstructured":"Zeng Y, Doshi P, Chen Y, Pan Y, Mao H, Chandrasekaran M (2016) Approximating behavioral equivalence for scaling solutions of i-dids. Knowl Inf Syst 49(2):511\u2013552","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"343_CR25","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s12293-020-00305-6","volume":"12","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Wong WK, Tan KC (2020) Competitive swarm optimizer with mutated agents for finding optimal designs for nonlinear regression models with multiple interacting factors. Memetic Comput 12(3):219\u2013233","journal-title":"Memetic Comput"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-021-00343-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-021-00343-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-021-00343-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T20:06:51Z","timestamp":1673122011000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-021-00343-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,21]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["343"],"URL":"https:\/\/doi.org\/10.1007\/s12293-021-00343-8","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,21]]},"assertion":[{"value":"15 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}