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| Title: | A connectionist approach to cost-based abduction |
| Author (s): | Abdelbar, Ashraf M. Andrews, Emad A.M. Wunsch, Donald C. |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering |
| Keywords: | Bayesian belief network High order network Neural network Probabilistic reasoning Recurrent network |
| Subject Terms: | Explanation. |
| Issue Date: | 2005-06 |
| Publisher: | Yang's Scientific Research Institute, LLC. |
| Citation: | Abdelbar, Ashraf M., Andrews, Emad A.M., and Wunsch, Donald C. "A Connectionist Approach to Cost-Based Abduction." International Journal of Computational Cognition, 3, (2005). |
| Abstract: | Cost-based abduction (CBA) is an important NP-hard problem in automated reasoning. In this formalism, 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 leastcost proof are taken as the best explanation for the given evidence. In this paper, we present a connectionist approach to cost-based abduction. We begin by reviewing high order recurrent networks (HORN) and their use in combinatorial optimization. We then formally define the cost-based abduction problem and describe previous work on this problem. This is followed by a description of how HORN’s can be applied to the CBA problem and experimental results on a 60-hypothesis, 65-rule CBA problem. We conclude with some remarks on the potential advantages of the connectionist approach. |
| Type: | Article - Journal text |
| In Title: | International Journal of Computational Cognition |
| 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 | A connectionist approach to cost-based abduction |
| contributor.author | Abdelbar, Ashraf M. |
| contributor.author | Andrews, Emad A.M. |
| contributor.author | Wunsch, Donald C. |
| contributor.deptlab | Applied Computational Intelligence Laboratory |
| contributor.deptlab | Electrical and Computer Engineering |
| subject | Bayesian belief network |
| subject | High order network |
| subject | Neural network |
| subject | Probabilistic reasoning |
| subject | Recurrent network |
| subject.LCSH | Explanation. |
| date.issued | 2005-06 |
| publisher | Yang's Scientific Research Institute, LLC. |
| identifier.citation | Abdelbar, Ashraf M., Andrews, Emad A.M., and Wunsch, Donald C. "A Connectionist Approach to Cost-Based Abduction." International Journal of Computational Cognition, 3, (2005). |
| identifier.pub.URI | |
| description.abstract | Cost-based abduction (CBA) is an important NP-hard problem in automated reasoning. In this formalism, 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 leastcost proof are taken as the best explanation for the given evidence. In this paper, we present a connectionist approach to cost-based abduction. We begin by reviewing high order recurrent networks (HORN) and their use in combinatorial optimization. We then formally define the cost-based abduction problem and describe previous work on this problem. This is followed by a description of how HORN’s can be applied to the CBA problem and experimental results on a 60-hypothesis, 65-rule CBA problem. We conclude with some remarks on the potential advantages of the connectionist approach. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Final version |
| 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 | International Journal of Computational Cognition |
| date.accessioned | 2007-04-11T17:00:48Z |
| date.available | 2008-03-19T18:47:36Z |
| identifier.persist.URI |