A First Step Towards Even More Sparse Encodings of Probability Distributions

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Abstract:Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.

Comments: Published in ILP2021. The final authenticated publication is available online at this https URL

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2603.29691 [cs.AI]

(or arXiv:2603.29691v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.29691

arXiv-issued DOI via DataCite (pending registration)

Journal reference: In: Katzouris, N., Artikis, A. (eds) Inductive Logic Programming. Lecture Notes in Computer Science, vol 13191. Springer, Cham (2022)

Related DOI:

https://doi.org/10.1007/978-3-030-97454-1_13

DOI(s) linking to related resources

Submission history

From: Florian Andreas Marwitz [view email] [v1] Tue, 31 Mar 2026 12:46:05 UTC (387 KB)