Finding least cost proofs using high order recurrent networks

Author

Abdelbar, Ashraf M. ; Andrews, Emad A M ; Tagliarini, Gene A.

Author_Institution

Dept. of Comput. Sci., American Univ. in Cairo, Egypt

Volume

4

fYear

2003

fDate

20-24 July 2003

Firstpage

2803

Abstract

Cost-based abduction (CBA) is an important AI formalism for representing knowledge under uncertainty. 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 least-cost proof are taken as the best explanation for the given evidence. The problem of finding the least-cost proof for a given CBA system is NP-hard and current techniques have exponential complexity in the worst case. Computational intelligence approaches to this problem have not been previously explored. In this paper, we show how high order recurrent networks can be used to find least-cost proofs for CBA instances. We describe experimental results on 80 CBA instances using networks of up to 68 neurons.

Keywords

formal logic; knowledge representation; recurrent neural nets; uncertainty handling; NP-hard problem; computational intelligence approaches; cost-based abduction; high order recurrent networks; least cost proofs; uncertain knowledge representation; Artificial intelligence; Bayesian methods; Computational intelligence; Computer science; Costs; Integer linear programming; Logic programming; Neurons; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1224015

Filename

1224015