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Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..ORCID iD: 0000-0002-1396-0102
Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs. While many approaches to structured prediction place strict constraints on the interactions between inferred variables, many real-world problems can be only characterized using complex graph structures of varying size, often contaminated with noise when obtained from real data. Here, we focus on one such problem in the domain of robotics. We demonstrate how GraphSPNs can be used to bolster inference about semantic, conceptual place descriptions using noisy topological relations discovered by a robot exploring large-scale office spaces. Through experiments, we show that GraphSPNs consistently outperform the traditional approach based on undirected graphical models, successfully disambiguating information in global semantic maps built from uncertain, noisy local evidence. We further exploit the probabilistic nature of the model to infer marginal distributions over semantic descriptions of as yet unexplored places and detect spatial environment configurations that are novel and incongruent with the known evidence.

Place, publisher, year, edition, pages
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE , 2018. p. 4547-4555
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-261987ISI: 000485488904078Scopus ID: 2-s2.0-85050353523OAI: oai:DiVA.org:kth-261987DiVA, id: diva2:1360144
Conference
32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, LA, FEB 02-07,2018
Note

QC 20191011

Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-11Bibliographically approved

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Pronobis, Andrzej

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
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Language
  • de-DE
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Output format
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