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A Glimpse into Statistical Relational AI: The Power of Indistinguishability

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Scalable Uncertainty Management (SUM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13562))

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Abstract

Statistical relational artificial intelligence, StaRAI for short, focuses on combining reasoning in uncertain environments with reasoning about individuals and relations in those environments. An important concept in StaRAI is indistinguishability, where groups of individuals behave indistinguishably in relation to each other in an environment. This indistinguishability manifests itself in symmetries in a propositional model and can be encoded compactly using logical constructs in relational models. Lifted inference then exploits indistinguishability for efficiency gains. This article showcases how to encode indistinguishability in models using logical constructs and highlights various ways of using indistinguishability during probabilistic inference.

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Acknowledgements

The author wishes to thank the SUM 2022 organisers for the invitation to give a tutorial on StaRAI and for the opportunity to write this article. The author also wishes to thank Marcel Gehrke and Ralf Möller for their continued collaboration and, with regards to this article, for collaborating on a tutorial at ECAI 2020, out of which this article grew.

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  1. Computer Science Department, University of Münster, Münster, Germany

    Tanya Braun

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  1. Tanya Braun
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Correspondence to Tanya Braun .

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  1. IRIT-Université Toulouse, Toulouse, France

    Florence Dupin de Saint-Cyr

  2. Université Paris-Dauphine - PSL, Paris, France

    Meltem Öztürk-Escoffier

  3. Imperial College London, London, UK

    Nico Potyka

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Braun, T. (2022). A Glimpse into Statistical Relational AI: The Power of Indistinguishability. In: Dupin de Saint-Cyr, F., Öztürk-Escoffier, M., Potyka, N. (eds) Scalable Uncertainty Management. SUM 2022. Lecture Notes in Computer Science(), vol 13562. Springer, Cham. https://doi.org/10.1007/978-3-031-18843-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-18843-5_1

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