{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:02Z","timestamp":1723016162340},"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":[[2021,8]]},"abstract":"<jats:p>Recent advances in symbolic dynamic programming (SDP) have significantly broadened the class of MDPs for which exact closed-form value functions can be derived. However, no existing solution methods can solve complex discrete and continuous state MDPs where a linear program determines state transitions --- transitions that are often required in problems with underlying constrained flow dynamics arising in problems ranging from traffic signal control to telecommunications bandwidth planning. In this paper, we present a novel SDP solution method for MDPs with LP transitions and continuous piecewise linear dynamics by introducing a novel, fully symbolic argmax operator. On three diverse domains, we show the first automated exact closed-form SDP solution to these challenging problems and the significant advantages of our SDP approach over discretized approximations.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/562","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"4083-4089","source":"Crossref","is-referenced-by-count":2,"title":["Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions"],"prefix":"10.24963","author":[{"given":"Jihwan","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, University of Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parth","family":"Jaggi","sequence":"additional","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, University of Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott","family":"Sanner","sequence":"additional","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, University of Toronto, Canada"},{"name":"Vector Institute, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:04:04Z","timestamp":1628679844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/562"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/562","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}