Abduction with probabilistic logic programming under the distribution semantics

Azzolini, Damiano; Bellodi, Elena; Ferilli, Stefano; Riguzzi, Fabrizio; Zese, Riccardo

doi:10.1016/j.ijar.2021.11.003

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…While, from a logical standpoint, all consistent abductive explanations are equally good, in a probabilistic setting different explanations of a goal are associated to different possible worlds, and their validity depends on the validity of the rules, facts and integrity constraints used to obtain those worlds . PEALP takes into account all these items [28,71]: Definition 18 ([28]). A Probabilistic Expressive Abductive Logic Program (PEALP) consists of a 4-tuple P, A, I, p , where P, A, I as in EALP and p : P ∪ ground(A) ∪ I → [0, 1] is a probability function.…”

Section: Abduction and Probabilistic Reasoningmentioning

confidence: 99%

GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Ferilli

2023

Electronics

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The pervasive use of AI today caused an urgent need for human-compliant AI approaches and solutions that can explain their behavior and decisions in human-understandable terms, especially in critical domains, so as to enforce trustworthiness and support accountability. The symbolic/logic approach to AI supports this need because it aims at reproducing human reasoning mechanisms. While much research has been carried out on single inference strategies, an overall approach to combine them is still missing. This paper claims the need for a new overall approach that merges all the single strategies, named MultiStrategy Reasoning. Based on an analysis of research on automated inference in AI, it selects a suitable setting for this approach, reviews the most promising approaches proposed for single inference strategies, and proposes a possible combination of deduction, abduction, abstraction, induction, argumentation, uncertainty and analogy. It also introduces the GEAR (General Engine for Automated Reasoning) inference engine, that has been developed to implement this vision.

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Section: Abduction and Probabilistic Reasoningmentioning

confidence: 99%

GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Ferilli

2023

Electronics

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“…ALP inherits the main limitation of LP, namely, the impossibility to reason with uncertain data. Recently, the authors of [39] introduced Probabilistic Abductive Logic Programs (PALPs), where PLP is extended with the possibility to define abducible facts with the syntax abducible a.…”

Section: Probabilistic Abductive Logic Programmingmentioning

confidence: 99%

Probabilistic Logic Models for the Lightning Network

Azzolini

Riguzzi

2022

Cryptography

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The Lightning Network (LN) has emerged as one of the prominent solutions to overcome the biggest limit of blockchain based on PoW: scalability. LN allows for creating a layer on top of an existing blockchain where users can send payments and micro-payments without waiting long confirmation times. One of the key features of LN is that payments can also be sent towards nodes that are not directly connected. From the routing perspective, the balance of an edge that connects two nodes is known, but the distribution between the two involved ends is unknown. Thus, the process of sending payments is based on a trial and error approach, and the routing can be considered probabilistic. Probabilistic Logic Programming (PLP) is a powerful formalism that allows the representation of complex relational domains characterized by uncertainty. In this paper, we study the problem of reasoning about the existence of a path between two nodes that can route a payment of a given size leveraging multiple models based on PLP. We adopt some recently proposed extensions of PLP and develop several models that can be adapted to represent multiple scenarios.

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Statistical Statements in Probabilistic Logic Programming

Azzolini

Bellodi

Riguzzi

2022

Lecture Notes in Computer Science

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Probabilistic Logic Programs under the distribution semantics (PLPDS) do not allow statistical probabilistic statements of the form “90% of birds fly”, which were defined “Type 1” statements by Halpern. In this paper, we add this kind of statements to PLPDS and introduce the PASTA (“Probabilistic Answer set programming for STAtistical probabilities”) language. We translate programs in our new formalism into probabilistic answer set programs under the credal semantics. This approach differs from previous proposals, such as the one based on “probabilistic conditionals” as, instead of choosing a single model by making the maximum entropy assumption, we take into consideration all models and we assign probability intervals to queries. In this way we refrain from making assumptions and we obtain a more neutral framework. We also propose an inference algorithm and compare it with an existing solver for probabilistic answer set programs on a number of programs of increasing size, showing that our solution is faster and can deal with larger instances.

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Abduction with probabilistic logic programming under the distribution semantics

Cited by 11 publications

References 29 publications

GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Probabilistic Logic Models for the Lightning Network

Statistical Statements in Probabilistic Logic Programming

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