Probabilistic Logical Inference on the Web

Alberti, Marco; Cota, Giuseppe; Riguzzi, Fabrizio; Zese, Riccardo

doi:10.1007/978-3-319-49130-1_26

Cited by 13 publications

(12 citation statements)

References 16 publications

(30 reference statements)

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“…In this paper, we presented an algorithm to solve the Maximum-A-Posteriori (MAP) and the Most-Probable-Explanation (MPE) problems on Logic Programs with Annotated Disjunctions. We integrated the algorithm into the PITA solver, which is available as a SWI-Prolog package and in the cplint on SWISH web application (Alberti et al 2016;Alberti et al 2017) at http://cplint.eu. We experimentally compared the algorithm with the ProbLog version (2.1) that supports annotated disjunctions and can perform the MAP and MPE tasks.…”

Section: Discussionmentioning

confidence: 99%

MAP Inference for Probabilistic Logic Programming

Bellodi

Alberti²,

Riguzzi³

et al. 2020

Theory and Practice of Logic Programming

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In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.

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Section: Discussionmentioning

confidence: 99%

MAP Inference for Probabilistic Logic Programming

Bellodi

Alberti²,

Riguzzi³

et al. 2020

Theory and Practice of Logic Programming

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“…cplint on SWISH uses the reasoning algorithms included in the cplint suite, including exact and approximate inference and parameter and structure learning. This article extends [ACRZ16], where we presented several algorithms for computing conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. The system also allowed hybrid programs, where some of the random variables are continuous, a feature that is, to the best of our knowledge, a novelty for web applications.…”

Section: Introductionmentioning

confidence: 90%

cplint on SWISH: Probabilistic Logical Inference with a Web Browser

Alberti

Bellodi

Cota

et al. 2017

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cplint on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting and particle filtering are used. cplint on SWISH is also able to sample goals' arguments and to graph the results. This paper reports on advances and new features of cplint on SWISH, including the capability of drawing the binary decision diagrams created during the inference processes.

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“…We consider Probabilistic Logic Programming (PLP) languages under the distribution semantics [21], that have been shown expressive enough to represent a wide variety of domains [2,19,1]. A program in a language adopting the distribution semantics defines a probability distribution over normal logic programs called instances or worlds.…”

Section: Probabilistic Logic Programmingmentioning

confidence: 99%

Proceedings 36th International Conference on Logic Programming (Technical Communications)

Gavanelli

Alberti

Lamma

2020

Electron. Proc. Theor. Comput. Sci.

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Bitcoin is one of the first decentralized, peer to peer, payment systems based on the so-called Proof-of-Work (PoW). PoW is an algorithm that requires the computation of a hard function in order to gain access to a resource but, at the same time, the correctness of the computed result should be easily checked. The use of a PoW removes the necessity of a centralized third party and so the consistency of the network may be altered directly by the involved users. Peers, to solve the PoW more efficiently, usually organize themselves into mining pools, to increase the overall computational power: this situation, unfortunately, leads to a network centralization. In this paper we consider two typical scenarios of a Bitcoin network and we model them by probabilistic logic programming (PLP): the centralization of the hashing power by large pools and the "double spending attack". In the first one, we verify the effectiveness of a protocol that attempts to discourage the formation of large pools. In the second one, we compute the probability of success of an attacker. Both scenarios are modeled using the PLP package cplint.

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Probabilistic Logical Inference on the Web

Cited by 13 publications

References 16 publications

MAP Inference for Probabilistic Logic Programming

MAP Inference for Probabilistic Logic Programming

cplint on SWISH: Probabilistic Logical Inference with a Web Browser

Proceedings 36th International Conference on Logic Programming (Technical Communications)

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