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|
|Title of host publication||IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning|
|Publication status||Published - 2009|
Bibliographical noteOther page information: -
Conference Proceedings/Title of Journal: IJCAI09 Workshop on Neural-Symbolic Learning and Reasoning
Other identifier: 2001072
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