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