{"type":"ScholarlyArticle","title":"Lifted Filtering via Exchangeable Decomposition","authors":[{"name":"Stefan Lüdtke","slug":"stefan-ludtke","given":"Stefan","family":"Lüdtke","affiliation":"Institute of Computer Science , University of Rostock , Germany"},{"name":"Max Schröder","slug":"max-schroder","given":"Max","family":"Schröder","affiliation":"Institute of Computer Science , University of Rostock , Germany"},{"name":"Sebastian Bader","slug":"sebastian-bader","given":"Sebastian","family":"Bader","affiliation":"Institute of Computer Science , University of Rostock , Germany"},{"name":"Kristian Kersting","slug":"kristian-kersting","given":"Kristian","family":"Kersting","affiliation":"Computer Science Department and Centre for Cognitive Science , TU Darmstadt , Germany"},{"name":"Thomas Kirste","slug":"thomas-kirste","given":"Thomas","family":"Kirste","affiliation":"Institute of Computer Science , University of Rostock , Germany"}],"abstract":"We present a model for exact recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations. The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable -- where the identity of entities does not matter -- it automatically uses a representation that abstracts from ordering (achieving an exponential reduction in complexity) -- and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry.","doi":"10.24963/ijcai.2018/703","url":"https://ijcai.org/proceedings/2018/703","pdf":"https://ijcai.org/proceedings/2018/0703.pdf","markdown":"https://ijcai.org/proceedings/2018/703.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/703","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Uncertainty in AI","track":"Main track","number":703,"pages":{"first":5067,"last":5073},"keywords":["Uncertainty in AI: Relational Inference","Uncertainty in AI: Exact Probabilistic Inference","Uncertainty in AI: Uncertainty in AI"],"videos":[],"references":[{"position":1,"raw":"[Barbuti et al., 2011] R. Barbuti, F. Levi, P. Milazzo, and G. Scatena. Maximally Parallel Probabilistic Seman- tics for Multiset Rewriting. Fundamenta Informaticae, 112(1):1-17, 2011.","title":"Maximally Parallel Probabilistic Semantics for Multiset Rewriting","authors":"R Barbuti, F Levi, P Milazzo, G Scatena","year":"2011","venue":"Fundamenta Informaticae","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boyen and Koller, 1998] Xavier Boyen and Daphne Koller. Tractable inference for complex stochastic processes. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 33-42, 1998.","title":"Tractable inference for complex stochastic processes","authors":"Koller Boyen, Xavier Boyen, Daphne Koller","year":"1998","venue":"Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bui et al., 2002] Hung Hai Bui, Svetha Venkatesh, and Ge- off West. Policy recognition in the abstract hidden markov model. Journal of Artificial Intelligence Research, 17:451-499, 2002.","title":"Policy recognition in the abstract hidden markov model","authors":"Hai Hung, Svetha Bui, Geoff Venkatesh, West","year":"2002","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bulling et al., 2014] Andreas Bulling, Ulf Blanke, and Bernt Schiele. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Sur- veys (CSUR), 46(3):33, 2014.","title":"A tutorial on human activity recognition using body-worn inertial sensors","authors":"Bulling","year":"2014","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cervesato et al., 1999] I. Cervesato, N. A. Durgin, P. D. Lincoln, J. C. Mitchell, and A. Scedrov. A Meta-Notation for Protocol Analysis. In Proceedings of the 12th IEEE Workshop on Computer Security Foundations, CSFW '99, pages 55-, Washington, DC, USA, 1999. IEEE Computer Society.","title":"A Meta-Notation for Protocol Analysis","authors":"Cervesato","year":"1999","venue":"Proceedings of the 12th IEEE Workshop on Computer Security Foundations, CSFW '99","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Choi et al., 2015] J. Choi, E. Amir, T. Xu, and A. Valoc- chi. Learning Relational Kalman Filtering. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelli- gence, pages 2539-2546, 2015.","title":"Learning Relational Kalman Filtering","authors":"J Choi, E Amir, T Xu, A","year":"2015","venue":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[De Raedt et al., 2016] L. De Raedt, K. Kersting, S. Natara- jan, and D. Poole. Statistical relational artificial intelli- gence: Logic, probability, and computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, 10(2):1-189, 2016.","title":"Statistical relational artificial intelligence: Logic, probability, and computation","authors":"L De Raedt, K Kersting, S Natarajan, D Poole","year":"2016","venue":"Synthesis Lectures on Artificial Intelligence and Machine Learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Doucet et al., 2000] A. Doucet, N. De Freitas, K. Murphy, and S. Russell. Rao-Blackwellised particle filtering for dy- namic Bayesian networks. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pages 176-183.","title":"Rao-Blackwellised particle filtering for dynamic Bayesian networks","authors":"A Doucet, N De Freitas, K Murphy, S Russell","year":null,"venue":"Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fox et al., 2003] V. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello. Bayesian filtering for location estima- tion. IEEE pervasive computing, 2(3):24-33, 2003.","title":"Bayesian filtering for location estimation","authors":"V Fox, J Hightower, L Liao, D Schulz, G Borriello","year":"2003","venue":"IEEE pervasive computing","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gogate and Domingos, 2012] Vibhav Gogate and Pedro Domingos. Probabilistic theorem proving. arXiv preprint arXiv:1202.3724, 2012.","title":"Probabilistic theorem proving","authors":"Vibhav Gogate, Pedro Domingos","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kasparick and Krüger, 2013] Martin Kasparick and Frank Krüger. Probabilistic action selection -tracking multi- ple persons in indoor environments. http://dx.doi. org/10.18453/rosdok_id00000114, 2013.","title":"Probabilistic action selection -tracking multiple persons in indoor environments","authors":"Krüger Kasparick, Martin Kasparick, Frank Krüger","year":"2013","venue":null,"doi":"10.18453/rosdok_id00000114,2013","url":"http://dx.doi.org/10.18453/rosdok_id00000114,2013","internal_url":null},{"position":12,"raw":"[Kersting et al., 2009] K. Kersting, B. Ahmadi, and S. Natarajan. Counting belief propagation. In Proceed- ings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 277-284, 2009.","title":"Counting belief propagation","authors":"K Kersting, B Ahmadi, S Natarajan","year":"2009","venue":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Krüger et al., 2014] Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Com- putational State Space Models for Activity and Inten- tion Recognition. A Feasibility Study. PLOS ONE, 9(11):e109381, November 2014.","title":"Computational State Space Models for Activity and Intention Recognition. A Feasibility Study","authors":"Krüger","year":"2014","venue":"PLOS ONE","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liao et al., 2007] Lin Liao, Donald J Patterson, Dieter Fox, and Henry Kautz. Learning and inferring transportation routines. Artificial Intelligence, 171(5-6):311-331, 2007.","title":"Learning and inferring transportation routines","authors":"Lin Liao, Donald J Patterson, Dieter Fox, Henry Kautz","year":"2007","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Niepert and Domingos, 2014] Mathias Niepert and Pedro Domingos. Exchangeable variable models. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 271-279, 2014.","title":"Exchangeable variable models","authors":"Domingos Niepert, Mathias Niepert, Pedro Domingos","year":"2014","venue":"Proceedings of the 31st International Conference on Machine Learning (ICML-14)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Niepert and Van den Broeck, 2014] Mathias Niepert and Guy Van den Broeck. Tractability through exchangeabil- ity: A new perspective on efficient probabilistic inference. In AAAI, pages 2467-2475, 2014.","title":"Tractability through exchangeability: A new perspective on efficient probabilistic inference","authors":"Van Niepert, Broeck Den, Mathias Niepert, Guy Van Den Broeck","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Nitti et al., 2013] D. Nitti, T. De Laet, and L. De Raedt. A particle filter for hybrid relational domains. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2764-2771. IEEE, November 2013.","title":"A particle filter for hybrid relational domains","authors":"D Nitti, T De Laet, L De Raedt","year":"2013","venue":"IEEE/RSJ International Conference on Intelligent Robots and Systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pescini et al., 2006] Dario Pescini, Daniela Besozzi, Gian- carlo Mauri, and Claudio Zandron. Dynamical probabilis- tic P systems. International Journal of Foundations of Computer Science, 17(01):183-204, 2006.","title":"Dynamical probabilistic P systems","authors":"Dario Pescini, Daniela Besozzi, Giancarlo Mauri, Claudio Zandron","year":"2006","venue":"International Journal of Foundations of Computer Science","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Pfeffer et al., 2009] Avi Pfeffer, Subrata Das, David Law- less, and Brenda Ng. Factored reasoning for monitoring dynamic team and goal formation. Information Fusion, 10(1):99-106, 2009.","title":"Factored reasoning for monitoring dynamic team and goal formation","authors":"Avi Pfeffer, Subrata Das, David Lawless, Brenda Ng","year":"2009","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Poole, 2003] D. Poole. First-order probabilistic inference. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, pages 985-991, 2003.","title":"First-order probabilistic inference","authors":"D Poole","year":"2003","venue":"Proceedings of the 18th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2003/141"},{"position":21,"raw":"[Poon and Domingos, 2011] Hoifung Poon and Pedro Domingos. Sum-product networks: A new deep architec- ture. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference On, pages 689-690. IEEE, 2011.","title":"Hoifung Poon and Pedro Domingos. Sum-product networks: A new deep architecture","authors":"Domingos Poon","year":"2011","venue":"Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference On","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sanner and Boutilier, 2009] S. Sanner and C. Boutilier. Practical solution techniques for first-order MDPs. Arti- ficial Intelligence, 173(5-6):748-788, April 2009.","title":"Practical solution techniques for first-order MDPs","authors":"S Sanner, C Boutilier","year":"2009","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Schröder et al., 2017] Max Schröder, Stefan Lüdtke, Sebas- tian Bader, Frank Krüger, and Thomas Kirste. Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems. In UAI Workshop: Statistical Relational Artificial Intelli- gence, 2017.","title":"Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems","authors":"Schröder","year":"2017","venue":"UAI Workshop: Statistical Relational Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Shirazi and Amir, 2011] Afsaneh Shirazi and Eyal Amir. First-order logical filtering. Artificial Intelligence, 175(1):193-219, 2011.","title":"First-order logical filtering","authors":"Afsaneh Shirazi, Eyal Amir","year":"2011","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Thon et al., 2011] I. Thon, N. Landwehr, and L. De Raedt. Stochastic relational processes: Efficient inference and ap- plications. Machine Learning, 82(2):239-272, February 2011.","title":"Stochastic relational processes: Efficient inference and applications","authors":"Thon","year":"2011","venue":"Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}