{"type":"ScholarlyArticle","title":"State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability","authors":[{"name":"Layton Hayes","slug":"layton-hayes","given":"Layton","family":"Hayes","affiliation":""},{"name":"Prashant Doshi","slug":"prashant-doshi","given":"Prashant","family":"Doshi","affiliation":""},{"name":"Swaraj Pawar","slug":"swaraj-pawar","given":"Swaraj","family":"Pawar","affiliation":""},{"name":"Hari Teja Tatavarti","slug":"hari-teja-tatavarti","given":"Hari Teja","family":"Tatavarti","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/348","url":"https://ijcai.org/proceedings/2021/348","pdf":"https://ijcai.org/proceedings/2021/0348.pdf","markdown":"https://ijcai.org/proceedings/2021/348.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/348","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":348,"pages":{"first":2526,"last":2533},"keywords":["Planning and Scheduling: Model-Based Reasoning","Planning and Scheduling: Planning under Uncertainty","Machine Learning: Learning Graphical Models"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}