A Neural Network Approach for First-Order Abductive Inference

Oliver Ray; Bruno Golenia

A Neural Network Approach for First-Order Abductive Inference

Oliver Ray, Bruno Golenia

Intelligent Systems Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 contribution	A Neural Network Approach for First-Order Abductive Inference
Original language	English
Title of host publication	IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning
Publication status	Published - 2009

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

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title = "A Neural Network Approach for First-Order Abductive Inference",

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. ",

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N2 - 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.

AB - 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.

M3 - Conference Contribution (Conference Proceeding)

BT - IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning

ER -

A Neural Network Approach for First-Order Abductive Inference

Abstract

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