Inference to the Stable Explanations

No Thumbnail Available
File version
Author(s)
Governatori, Guido
Olivieri, Francesco
Rotolo, Antonino
Cristani, Matteo
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)

Gottlob, G

Inclezan, D

Maratea, M

Date
2022
Size
File type(s)
Location

Italy, Genova

License
Abstract

The process of explaining a piece of evidence by constructing a set of assumptions that are a good explanation for that evidence is ubiquitous in real-life (e.g. in legal systems). In this paper, we introduce, discuss, and formalise the notion of stable explanations in a non-monotonic setting. We show how, while applying it to the process of (1) computing a set of literals able to (2) derive a conclusion (3) from a set of defeasible rules, we obtain a restricted version of the notion of abduction. This is both interesting and useful: when an explanation for a given conclusion is stable, it can, in fact, be used to infer the same conclusion independently of other pieces of evidence that are found afterwards.

Journal Title
Conference Title

LPNMR 2022: Logic Programming and Nonmonotonic Reasoning

Book Title
Edition
Volume

13416

Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Logic

ABDUCTION

COMPLEXITY

Computer Science

Computer Science, Artificial Intelligence

Computer Science, Software Engineering

Persistent link to this record
Citation

Governatori, G; Olivieri, F; Rotolo, A; Cristani, M, Inference to the Stable Explanations, LPNMR 2022: Logic Programming and Nonmonotonic Reasoning, 2022, 13416, pp. 245-258