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Generative Datalog and Answer Set Programming – Extended Abstract

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Logics in Artificial Intelligence (JELIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14281))

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Abstract

Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a stochastic process, resulting in a declarative formalism suitable for modeling and analyzing other stochastic processes. This work provides an introduction to Generative Datalog through the lens of Answer Set Programming (ASP), demonstrating how Generative Datalog can explain the output of ASP systems that include @-terms referencing probability distributions. From a theoretical point of view, extending the semantics of Generative Datalog to stable negation proved to be challenging due to the richness of ASP relative to Datalog in terms of linguistic constructs. On a more pragmatic side, the connection between the two formalisms lays the foundation for implementing Generative Datalog atop efficient ASP systems, making it a practical solution for real-world applications.

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Acknowledgments

This work is about some ongoing research with Matthias Lanzinger, Michael Morak, and Andreas Pieris [2]. This work was partially supported by Italian Ministry of Research (MUR) under PNRR project FAIR “Future AI Research”, CUP H23C22000860006, under PNRR project Tech4You “Technologies for climate change adaptation and quality of life improvement”, CUP H23C22000370006, and under PNRR project SERICS “SEcurity and RIghts in the CyberSpace”, CUP H73C22000880001; by the LAIA lab (part of the SILA labs) and by GNCS-INdAM.

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Authors and Affiliations

  1. DEMACS, University of Calabria, Via Bucci 30/B, 87036, Rende, CS, Italy

    Mario Alviano

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  1. Mario Alviano
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Correspondence to Mario Alviano .

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Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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Alviano, M. (2023). Generative Datalog and Answer Set Programming – Extended Abstract. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-43619-2_1

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