Probabilistic sentence satisfiability: An approach to PSAT

Henderson, Thomas C.; Simmons, Robert F.; Serbinowski, Bernard; Cline, Michael; Sacharny, David; Fan, Xiangyu; Mitiche, Amar

doi:10.1016/j.artint.2019.103199

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Henderson et.al [9,21,22] discuss the case when sentences are independent. In such cases, for any two sentences σ 0 , σ 1 in a language, Pr(σ 0 ∧ σ 1 ) = Pr(σ 0 ) Pr(σ 1 ).…”

Section: Fan / Relaxing Rule Constraints In Probabilistic Assumption-...mentioning

confidence: 99%

“…Given a language L with n sentences, the Complete Conjunction Set (CC Set) [9] of L is the set of 2 n conjunction of sentences such that each conjunction contains n distinct sentences. For instance, for…”

Section: Introductionmentioning

confidence: 99%

“…However, both are designed for solving the full joint probability distribution over the CC set 8. Here, stands for an anonymous variable as in Prolog 9. On this note, PABA also forces independence between their probabilistic parameters by having Pr(ω) = (Q,[a:x])∈GE ω x, where (Q, [a : x]) is a deduction for a and GEω is the set of grounded extensions containing all possible worlds, in which a possible world ω is an element in the CC set of all probabilistic parameters (Definition 2.1 and Lemma 2.1 in[1]).…”

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Rule-PSAT: Relaxing Rule Constraints in Probabilistic Assumption-Based Argumentation

Fan

2022

Computational Models of Argument

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Probabilistic rules are at the core of probabilistic structured argumentation. With a language L, probabilistic rules describe conditional probabilities Pr(σ0|σ1,…,σk) of deducing some sentences σ0∈L from others σ1,…,σk∈L by means of prescribing rules σ0←σ1,…,σk with head σ0 and body σ1,…,σk. In Probabilistic Assumption-based Argumentation (PABA), a few constraints are imposed on the form of probabilistic rules. Namely, (1) probabilistic rules in a PABA framework must be acyclic, and (2) if two rules have the same head, then the body of one rule must be the subset of the other. In this work, we show that both constraints can be relaxed by introducing the concept of Rule Probabilistic Satisfiability (Rule-PSAT) and solving the underlying joint probability distribution on all sentences in L. A linear programming approach is presented for solving Rule-PSAT and computing sentence probabilities from joint probability distributions.

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“…Henderson et.al [9,21,22] discuss the case when sentences are independent. In such cases, for any two sentences σ 0 , σ 1 in a language, Pr(σ 0 ∧ σ 1 ) = Pr(σ 0 ) Pr(σ 1 ).…”

Section: Fan / Relaxing Rule Constraints In Probabilistic Assumption-...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rule-PSAT: Relaxing Rule Constraints in Probabilistic Assumption-Based Argumentation

Fan

2022

Computational Models of Argument

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“…Probabilistic inference is a difficult problem in Artificial Intelligence [37]. The use of logic remains to play an important role in AI [51,79], even though there are successful techniques that do not require modeling or reasoning such as neural networks [30]. Inference by Weighted Model Counting (WMC) is a logical approach that has been very…”

Section: Related Workmentioning

confidence: 99%

Probabilistic inference using partitioned bayesian networks : introducing a compositional framework

Dal

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Previous chapters have introduced many theoretical concepts that help improve the performance of probabilistic inference. These concepts were implemented in a tool called ParaGnosis, an open-source tool that supports inference queries on discrete Bayesian networks through WMC. This tool can be found on GitHub 1 and Zenodo 2 to ensure long-term availability and has a DOI 3 . ParaGnosis was awarded the Functional, Reusable and Available badges by TACAS 2023: the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. This chapter is based on [39]. My contributions are the writing of the paper and I am the creator of ParaGnosis, where concepts are implemented from [40-43]. Chapter 8: Model-based Probabilistic Diagnosis in Large ModelsModel-based diagnostics or diagnosis is concerned with diagnosing faults or malfunction of physical or cyber-physical systems using a model of the structure and behavior of a real-world system. As cyber-physical systems can be extremely large and complex the associated models can be equally large and complex, imposing a challenge on the computational feasibility of reasoning with such models. In this chapter, we create such a system and evaluate its performance and diagnostic behavior.This chapter is based on a submitted article. My contributions are the writing of the paper and performing the empirical evaluation using ParaGnosis. Chapter 9: Discussion And ConclusionsResearch presented in this thesis is briefly summarized, and brought into context of what was achieved and other research. We also look toward future work and hypothesize on promising research directions.

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