A Neural Network Approach for First-Order Abductive Inference

Oliver Ray, Bruno Golenia

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

This paper presents a neural network approach for first-order abductive inference by generalising an existing method from propositional logic to the first-order case. We show how the original propositional method can be extended to enable the grounding of a first-order abductive problem; and we also show how it can be modified to allow the prioritised computation of minimal solutions. We illustrate the approach on a well-known abductive problem and explain how it can be used to perform first-order conditional query answering.
Translated title of the contributionA Neural Network Approach for First-Order Abductive Inference
Original languageEnglish
Title of host publicationIJCAI09 Workshop on Neural-Symbolic Learning and Reasoning
Publication statusPublished - 2009

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning
Other identifier: 2001072

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    Ray, O., & Golenia, B. (2009). A Neural Network Approach for First-Order Abductive Inference. In IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2001072