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plingo: A system for probabilistic reasoning in clingo based on lpmln

arXiv 2206.11515 ↗First posted 23 Jun 2022cs.AIcited by 0

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Abstract (arXiv)

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 23 Jun 2022.

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References · 7

  1. 2019ASP-Core-2 Input Language Formatcited by 77
  2. 2019Explainable Fact Checking with Probabilistic Answer Set Programmingcited by 23
  3. 2020How to build your own ASP-based system?!cited by 20
  4. 2017Computing LPMLN Using ASP and MLN Solverscited by 8
  5. 2021SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentationcited by 5
  6. 2018Weight Learning in a Probabilistic Extension of Answer Set Programscited by 4
  7. 2021Solution Enumeration by Optimality in Answer Set Programmingcited by 2

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