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| Title: | Abductive reasoning with recurrent neural networks |
| Author (s): | Wunsch, Donald C. |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering |
| Keywords: | Abductive Neural Networks Reasoning |
| Issue Date: | 2003-07 |
| Publisher: | Elsevier |
| Citation: | Abdelbar M. Ashraf, Emad A.M. Andrews, and Donald C. Wunsch II, "Abductive Reasoning with Recurrent Neural Networks,” Neural Networks, July 2003. |
| Abstract: | Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In previous work, we presented a method for using high order recurrent networks to find least cost proofs for CBA instances. Here, we present a method that significantly reduces the size of the neural network that is produced for a given CBA instance. We present experimental results describing the performance of this method and comparing its performance to that of the previous method. |
| Type: | Article - Journal text |
| In Title: | Neural Networks |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder FULL COPYRIGHT INFORMATION: |
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| title | Abductive reasoning with recurrent neural networks |
| contributor.author | Wunsch, Donald C. |
| contributor.deptlab | Applied Computational Intelligence Laboratory |
| contributor.deptlab | Electrical and Computer Engineering |
| subject | Abductive |
| subject | Neural Networks |
| subject | Reasoning |
| date.issued | 2003-07 |
| publisher | Elsevier |
| identifier.citation | Abdelbar M. Ashraf, Emad A.M. Andrews, and Donald C. Wunsch II, "Abductive Reasoning with Recurrent Neural Networks,” Neural Networks, July 2003. |
| identifier.pub.URI | |
| description.abstract | Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In previous work, we presented a method for using high order recurrent networks to find least cost proofs for CBA instances. Here, we present a method that significantly reduces the size of the neural network that is produced for a given CBA instance. We present experimental results describing the performance of this method and comparing its performance to that of the previous method. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder |
| rights.URI | |
| relation.isPartOf | Neural Networks |
| date.accessioned | 2007-04-11T17:00:48Z |
| date.available | 2008-03-24T20:29:22Z |
| identifier.persist.URI |