A Connectionist Approach to Cost-Based Abduction

Ashraf M. Abdelbar; Donald C. Wunsch; Emad A. M. Andrews

A Connectionist Approach to Cost-Based Abduction

Ashraf M. Abdelbar
, Donald C. Wunsch
, Emad A. M. Andrews

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	International Journal of Computational Cognition
State	Published - Jun 1 2005

Keywords

Bayesian Belief Network
Explanation
High Order Network
Neural Network
Probabilistic Reasoning
Recurrent Network

Disciplines

Electrical and Computer Engineering

Access to Document

http://www.yangsky.com/ijcc/pdf/ijcc326.pdf

Cite this

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title = "A Connectionist Approach to Cost-Based Abduction",

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.",

keywords = "Bayesian Belief Network, Explanation, High Order Network, Neural Network, Probabilistic Reasoning, Recurrent Network",

author = "Abdelbar, \{Ashraf M.\} and Wunsch, \{Donald C.\} and Andrews, \{Emad A. M.\}",

year = "2005",

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day = "1",

language = "American English",

journal = "International Journal of Computational Cognition",

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TY - JOUR

T1 - A Connectionist Approach to Cost-Based Abduction

AU - Abdelbar, Ashraf M.

AU - Wunsch, Donald C.

AU - Andrews, Emad A. M.

PY - 2005/6/1

Y1 - 2005/6/1

N2 - 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.

AB - 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.

KW - Bayesian Belief Network

KW - Explanation

KW - High Order Network

KW - Neural Network

KW - Probabilistic Reasoning

KW - Recurrent Network

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/409

UR - http://www.yangsky.com/ijcc/pdf/ijcc326.pdf

M3 - Article

JO - International Journal of Computational Cognition

JF - International Journal of Computational Cognition

ER -

A Connectionist Approach to Cost-Based Abduction

Abstract

Keywords

Disciplines

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