{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:37:54Z","timestamp":1778600274843,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response accordingly. Previous works usually assume an opponent uses a stationary strategy or randomly switches among several stationary ones. However, an opponent may exhibit more sophisticated behaviors by adopting more advanced reasoning strategies, e.g., using a Bayesian reasoning strategy. This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies. Bayes-ToMoP also supports the detection of previously unseen policies and learning a best-response policy accordingly. We provide a theoretical guarantee of the optimality on detecting the opponent's strategies. We also propose a deep version of Bayes-ToMoP by extending Bayes-ToMoP with DRL techniques. Experimental results show both Bayes-ToMoP and deep Bayes-ToMoP outperform the state-of-the-art approaches when faced with different types of opponents in two-agent competitive games.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/88","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"623-629","source":"Crossref","is-referenced-by-count":31,"title":["Towards Efficient Detection and Optimal Response against Sophisticated Opponents"],"prefix":"10.24963","author":[{"given":"Tianpei","family":"Yang","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianye","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaopeng","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"MMW, Tsinghua University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beifang Investigation, Design & Research CO.LTD"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:42Z","timestamp":1564285602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/88"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/88","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}