A glimpse of symbolic-statistical modeling by PRISM

Sato, Taisuke

doi:10.1007/s10844-008-0062-7

Cited by 90 publications

(112 citation statements)

References 26 publications

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“…Closely related to [29] is [49], in which probability distributions are attached to sets of literals capturing alternative scenarios and logic programming is used to generate a distribution on possible worlds. In [53], Prolog is extended with probabilistic switches to define distributions over Herbrand interpretations. In [3], A-Prolog [20] is extended with probabilistic atoms to generate distributions on possible worlds using answer set programming.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic modelling, inference and learning using logical theories

Lloyd

Uther

2008

Ann Math Artif Intell

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This paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.

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Section: Discussionmentioning

confidence: 99%

Probabilistic modelling, inference and learning using logical theories

Lloyd

Uther

2008

Ann Math Artif Intell

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“…Our proposal is inspired by this potential-based model of probabilistic argumentation, but for abstract argumentation combined with energy-based undirected graphical models, and in particular with the graphical model of BMs. Sneyers et al (2013) tackled a similar setting of Riveret et al (2007) (but without the concern of reflecting the structure of dialogues) by defining a probabilistic argumentation logic and implementing it with the language CHRiSM (Sneyers, Meert, Vennekens, Kameya, and Sato, 2010), a rule-based probabilistic logic programming language based on constraint handling rules (CHR) (Fruhwirth, 2009) associated with a high-level probabilistic programming language called PRISM (Sato, 2008). This language must not be confused with the system PRISM for probabilistic model checking developed by Hinton et al (2006).…”

Section: Motivations and Applicationsmentioning

confidence: 99%

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Riveret¹,

Korkinof²,

Draief³

et al. 2015

Argument & Computation

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Probabilistic argumentation and neuro-argumentative systems offer new computational perspectives for the theory and applications of argumentation, but their principled construction involves two entangled problems. On the one hand, probabilistic argumentation aims at combining the quantitative uncertainty addressed by probability theory with the qualitative uncertainty of argumentation, but probabilistic dependences amongst arguments as well as learning are usually neglected. On the other hand, neuro-argumentative systems offer the opportunity to couple the computational advantages of learning and massive parallel computation from neural networks with argumentative reasoning and explanatory abilities, but the relation of probabilistic argumentation frameworks with these systems has been ignored so far. Towards the construction of neuro-argumentative systems based on probabilistic argumentation, we associate a model of abstract argumentation and the graphical model of Boltzmann machines (BMs) in order to (i) account for probabilistic abstract argumentation with possible and unknown probabilistic dependences amongst arguments, (ii) learn distributions of labellings from a set of cases and (iii) sample labellings according to the learned distribution. Experiments on domain independent artificial datasets show that argumentative BMs can be trained with conventional training procedures and compare well with conventional machines for generating labellings of arguments, with the assurance of generating grounded labellingson demand.

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“…ProbLog (Gutmann et al, 2008) was motivated by problems in modelling probabilistic relational databases. Stochastic logic programs (SLP) (Muggleton, 2000) and PRISM (Sato and Kameya, 1997) aim at generalising both probabilistic grammars and logic programs. Programs of such formalisms are referred to as probabilistic logic programs (PLPs).…”

Section: Probabilistic Logic Programsmentioning

confidence: 99%

Learning failure-free PRISM programs

Alsanie

Cussens

2015

International Journal of Approximate Reasoning

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First-order logic can be used to represent relations amongst objects. Probabilistic graphical models encode uncertainty over propositional data. Following the demand of combining the advantages of both representations, probabilistic logic programs provide the ability to encode uncertainty over relational data. PRISM is a probabilistic logic programming formalism based on the distribution semantics. PRISM allows learning the parameters when the programs are known.This thesis proposes algorithms to learn failure-free PRISM programs. It combines ideas from both areas of inductive logic programming and learning Bayesian networks. The learned PRISM programs generalise dynamic Bayesian networks by defining a halting distribution over the sampling process. Each dynamic Bayesian network models either an infinite sequential generative process or a sequential generative process of a fixed length. In both cases, only a fixed length of sequences can be sampled. On the other hand, the PRISM programs considered in this thesis represent self-terminating functions from which sequences of different lengths can be obtained. The effectiveness of the proposed algorithms on learning five programs is shown.

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A glimpse of symbolic-statistical modeling by PRISM

Cited by 90 publications

References 26 publications

Probabilistic modelling, inference and learning using logical theories

Probabilistic modelling, inference and learning using logical theories

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Learning failure-free PRISM programs

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