{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:46:32Z","timestamp":1774446392846,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>\n      \n        Many real world security problems can be modelled as finite zero-sum games with structured sequential strategies and limited interactions between the players. An abstract class of games unifying these models are the normal-form games with sequential strategies (NFGSS). We show that all games from this class can be modelled as well-formed imperfect-recall extensive-form games and consequently can be solved by counterfactual regret minimization. We propose an adaptation of the CFR+ algorithm for NFGSS and compare its performance to the standard methods based on linear programming and incremental game generation. We validate our approach on two security-inspired domains.  We show that with a negligible loss in precision, CFR+ can compute a Nash equilibrium with five times less computation than its competitors.\n      \n    <\/jats:p>","DOI":"10.1609\/aaai.v30i1.10051","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T06:05:18Z","timestamp":1656050718000},"source":"Crossref","is-referenced-by-count":8,"title":["Counterfactual Regret Minimization in Sequential Security Games"],"prefix":"10.1609","volume":"30","author":[{"given":"Viliam","family":"Lisy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor","family":"Davis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Bowling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2016,2,21]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/10051\/9910","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/10051\/9910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T06:05:18Z","timestamp":1656050718000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/10051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,21]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,2,18]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v30i1.10051","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2016,2,21]]}}}