Title

Belief-evidence fusion in a hybrid intelligent system

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004

Volume

1

First Page

322

Last Page

329

Publication Date

11-2-2004

Abstract

A hybrid intelligent system that is able to successively refine knowledge stored in its rulebase is developed. The existing knowledge (referred to as belief rules), which may initially be defined by experts in a particular domain, is stored in the form of rules in the rulebase and is refined by comparing it with new knowledge (referred to as evidence rules) extracted from data sets trained under a neural network. Based on measurement, assessment, and interpretation of rule similarity, belief rules existing in the rulebase may be found to be confirmed, contradicted, or left unsupported by new training data. New evidence rules may also be discovered from the training data set. This rule comparison is unique in the sense that rules are viewed and compared in a geometric manner. As rules evolve in existence in the rulebase during the belief-evidence fusion process, their bounds, strengths, and certainties are also revised. The hybrid intelligent system is tested with different data sets, including hypothetical data sets and actual data sets.

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Disciplines

Artificial Intelligence and Robotics

Keywords

Artificial intelligence; Hybrid systems

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