CHR(PRISM)-based probabilistic logic learning

Sneyers, Jon; Meert, Wannes; Vennekens, Joost; Kameya, Yoshitaka; Sato, Taisuke

doi:10.1017/s1471068410000207

Cited by 13 publications

(18 citation statements)

References 17 publications

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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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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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“…The system CHRat [76] implements a modular version of CHR that allows for reusing built-in constraints, defined in a constraint system, as a constraint solver in another CHR program (http: //contraintes.inria.fr/˜tmartine/chrat/). CHRiSM [198] (http: //people.cs.kuleuven.be/˜jon.sneyers/chrism/) integrates CHR and PRISM (PRogramming In Statistical Modeling) [190], a probabilistic extension of Prolog for symbolic-statistical modeling.…”

Section: Interpreters For Ccp Calculimentioning

confidence: 99%

Models and emerging trends of concurrent constraint programming

2013

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Concurrent Constraint Programming (CCP) has been used over the last two decades as an elegant and expressive model for concurrent systems. It models systems of agents communicating by posting and querying partial information, represented as constraints over the variables of the system. This covers a vast variety of systems as those arising in biological phenomena, reactive systems, netcentric computing and the advent of social networks and cloud computing. In this paper we survey the main applications, developments and current trends of CCP.

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“…Basically, in the distribution semantics all facts are assumed to be mutually independent [Hommersom and Lucas 2011]. Similar assumptions are made in certain other logics such as Independent Choice Logic [Poole 2008] and PRISM [Sato 1995;Sneyers et al 2010]. Subrahmanian [1992, 1993] propose probabilistic logic programs where the independence assumption is not required, but this is computationally expensive though recent approaches [Khuller et al 2007] based on sampling have been shown to scale very well to the case of 100K atoms.…”

Section: Related Work In Logic Programmingmentioning

confidence: 99%

“…In many logics that incorporate independence assumptions including [Poole 2008;Raedt et al 2007;Sneyers et al 2010], the probability of diffusion from neighbors of a vertex to a vertex are computed via the independence assumption. In the simplest sense, consider a vertex v and two vertices a, b such that (a, v), (b, v) are edges in the graph and suppose there are no other edges of the form (−, v).…”

Section: Related Work In Logic Programmingmentioning

confidence: 99%

“…However, though GAPs can be more expressive than many languages such as [Poole 2008;Raedt et al 2007;Sneyers et al 2010], there is no guarantee that they will be more "efficient" or more "intuitive" when additional assumptions such as independence are made. For instance, suppose we consider a more complex situation where we have three vertices a, b, c and the same vertex v before.…”

Section: Related Work In Logic Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems

Shakarian

Broecheler

Subrahmanian

et al. 2013

ACM Trans. Comput. Logic

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There has been extensive work in many different fields on how phenomena of interest (e.g., diseases, innovation, product adoption) "diffuse" through a social network. As social networks increasingly become a fabric of society, there is a need to make "optimal" decisions with respect to an observed model of diffusion. For example, in epidemiology, officials want to find a set of k individuals in a social network which, if treated, would minimize spread of a disease. In marketing, campaign managers try to identify a set of k customers that, if given a free sample, would generate maximal "buzz" about the product. In this article, we first show that the well-known Generalized Annotated Program (GAP) paradigm can be used to express many existing diffusion models. We then define a class of problems called Social Network Diffusion Optimization Problems (SNDOPs). SNDOPs have four parts: (i) a diffusion model expressed as a GAP, (ii) an objective function we want to optimize with respect to a given diffusion model, (iii) an integer k > 0 describing resources (e.g., medication) that can be placed at nodes, (iv) a logical condition VC that governs which nodes can have a resource (e.g., only children above the age of 5 can be treated with a given medication). We study the computational complexity of SNDOPs and show both NP-completeness results as well as results on complexity of approximation. We then develop an exact and a heuristic algorithm to solve a large class of SNDOPproblems and show that our GREEDY-SNDOP algorithm achieves the best possible approximation ratio that a polynomial algorithm can achieve (unless P = NP). We conclude with a prototype experimental implementation to solve SNDOPs that looks at a real-world Wikipedia dataset consisting of over 103,000 edges. ACM Reference Format:Shakarian, P., Broecheler, M., Subrahmanian, V. S. and Molinaro, C. 2013. Using generalized annotated programs to solve social network diffusion optimization problems.

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CHR(PRISM)-based probabilistic logic learning

Cited by 13 publications

References 17 publications

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Models and emerging trends of concurrent constraint programming

Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems

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