{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:42:26Z","timestamp":1723016546014},"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>The sum-product network (SPN) has been extended to model sequence data with the recurrent SPN (RSPN), and to decision-making problems with sum-product-max networks (SPMN). In this paper, we build on the concepts introduced by these extensions and present state-based recurrent SPMNs (S-RSPMNs) as a generalization of SPMNs to sequential decision-making problems where the state may not be perfectly observed. As with recurrent SPNs, S-RSPMNs utilize a repeatable template network to model sequences of arbitrary lengths. We present an algorithm for learning compact template structures by identifying unique belief states and the transitions between them through a state matching process that utilizes augmented data.  In our knowledge, this is the first data-driven approach that learns graphical models for planning under partial observability, which can be solved efficiently. S-RSPMNs retain the linear solution complexity of SPMNs, and we demonstrate significant improvements in compactness of representation and the run time of structure learning and inference in sequential domains.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/348","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2526-2533","source":"Crossref","is-referenced-by-count":0,"title":["State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability"],"prefix":"10.24963","author":[{"given":"Layton","family":"Hayes","sequence":"first","affiliation":[{"name":"Institute for AI, University of Georgia, Athens GA 30602"}]},{"given":"Prashant","family":"Doshi","sequence":"additional","affiliation":[{"name":"Institute for AI, University of Georgia, Athens GA 30602"},{"name":"Department of Computer Science, University of Georgia, Athens GA 30602"}]},{"given":"Swaraj","family":"Pawar","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science, University of Georgia, Athens GA 30602"}]},{"given":"Hari Teja","family":"Tatavarti","sequence":"additional","affiliation":[{"name":"Institute for AI, University of Georgia, Athens GA 30602"}]}],"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":"2021-08-11T11:02:46Z","timestamp":1628679766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/348"}},"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\/348","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}