{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:36Z","timestamp":1723016196238},"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":[[2018,7]]},"abstract":"<jats:p>Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. \u00a0This paper deals with the relatively under-explored but equally important \"inverse\" setting, where the parameters of the underlying game are not known to all agents, but must be learned through observations. \u00a0We propose a differentiable, end-to-end learning framework for addressing this task. \u00a0In particular, we consider a regularized version of the game, equivalent to a particular form of quantal response equilibrium, and develop 1) a primal-dual Newton method for finding such equilibrium points in both normal and extensive form games; and 2) a backpropagation method that lets us analytically compute gradients of all relevant game parameters through the solution itself. \u00a0This ultimately lets us learn the game by training in an end-to-end fashion, effectively by integrating a \"differentiable game solver\" into the loop of larger deep network architectures. We demonstrate the effectiveness of the learning method in several settings including poker and security game tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/55","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"396-402","source":"Crossref","is-referenced-by-count":12,"title":["What Game Are We Playing?  End-to-end Learning in Normal and Extensive Form Games"],"prefix":"10.24963","author":[{"given":"Chun Kai","family":"Ling","sequence":"first","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. Zico","family":"Kolter","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:34Z","timestamp":1530769774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/55"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/55","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}