smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

• URL: http://arxiv.org/abs/2304.00879v2
• Date: Mon, 17 Apr 2023 09:21:03 GMT
• Title: smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
• Authors: Pietro Totis, Angelika Kimmig, Luc De Raedt
• Abstract summary: We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics. The second contribution is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution is the implementation of a PLP system supporting this semantics: smProbLog.
• Score: 19.46250467634934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modelling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic programming language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.

Related papers

• Probabilistic Answer Set Programming with Discrete and Continuous Random Variables [0.18416014644193066]
  Probabilistic Answer Set Programming (PASP) extends Answer Set Programming with probabilistic facts that represent uncertain information. We propose Hybrid Probabilistic Answer Set Programming (HPASP) We discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation.
  arXiv  Detail & Related papers  (2024-09-30T13:24:42Z)
• LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers [60.009969929857704]
  Logical reasoning is an important task for artificial intelligence with potential impacts on science, mathematics, and society. In this work, we reformulating such tasks as modular neurosymbolic programming, which we call LINC. We observe significant performance gains on FOLIO and a balanced subset of ProofWriter for three different models in nearly all experimental conditions we evaluate.
  arXiv  Detail & Related papers  (2023-10-23T17:58:40Z)
• "What if?" in Probabilistic Logic Programming [2.9005223064604078]
  A ProbLog program is a logic program with facts that only hold with a specified probability. We extend this ProbLog language by the ability to answer "What if" queries.
  arXiv  Detail & Related papers  (2023-05-24T16:35:24Z)
• $\omega$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs [64.25762042361839]
  $omega$PAP spaces are spaces for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is general enough to assign meanings to most practical probabilistic and differentiable programs. We establish the almost-everywhere differentiability of probabilistic programs' trace density functions.
  arXiv  Detail & Related papers  (2023-02-21T12:50:05Z)
• Machine Learning with Probabilistic Law Discovery: A Concise Introduction [77.34726150561087]
  Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. This paper outlines the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
  arXiv  Detail & Related papers  (2022-12-22T17:40:13Z)
• Probabilistic Deduction: an Approach to Probabilistic Structured Argumentation [1.027974860479791]
  Probabilistic Deduction (PD) is an approach to probabilistic structured argumentation. PD provides a framework to unify probabilistic reasoning with argumentative reasoning. This is the first work in probabilistic structured argumentation where the joint distribution is not assumed form external sources.
  arXiv  Detail & Related papers  (2022-09-01T03:58:38Z)
• SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation [17.71804768917815]
  SMProbLog is a generalization of the probabilistic logic programming language ProbLog. We show how SMProbLog can be used to reason about probabilistic argumentation problems.
  arXiv  Detail & Related papers  (2021-10-05T12:29:22Z)
• Logical Credal Networks [87.25387518070411]
  This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability. We investigate its performance on maximum a posteriori inference tasks, including solving Mastermind games with uncertainty and detecting credit card fraud.
  arXiv  Detail & Related papers  (2021-09-25T00:00:47Z)
• Non-ground Abductive Logic Programming with Probabilistic Integrity Constraints [2.624902795082451]
  In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. We first present the overall abductive language, and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.
  arXiv  Detail & Related papers  (2021-08-06T10:22:12Z)
• Online Learning Probabilistic Event Calculus Theories in Answer Set Programming [70.06301658267125]
  Event Recognition (CER) systems detect occurrences in streaming time-stamped datasets using predefined event patterns. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of rules weighted in the Event Calculus. Our results demonstrate the superiority of our novel approach, both terms efficiency and predictive.
  arXiv  Detail & Related papers  (2021-03-31T23:16:29Z)
• Tractable Inference in Credal Sentential Decision Diagrams [116.6516175350871]
  Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. We develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with credal sets of mass functions. For a first empirical validation, we consider a simple application based on noisy seven-segment display images.
  arXiv  Detail & Related papers  (2020-08-19T16:04:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.