Belief-evidence fusion in a hybrid intelligent system

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 comp...

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Main Authors: Marcos, Nelson, Azcarraga, Arnulfo P.
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Published: Animo Repository 2004
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2156
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-31552022-11-16T02:31:22Z Belief-evidence fusion in a hybrid intelligent system Marcos, Nelson Azcarraga, Arnulfo P. 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. 2004-11-02T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2156 Faculty Research Work Animo Repository Artificial intelligence Hybrid systems Artificial Intelligence and Robotics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Artificial intelligence
Hybrid systems
Artificial Intelligence and Robotics
spellingShingle Artificial intelligence
Hybrid systems
Artificial Intelligence and Robotics
Marcos, Nelson
Azcarraga, Arnulfo P.
Belief-evidence fusion in a hybrid intelligent system
description 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.
format text
author Marcos, Nelson
Azcarraga, Arnulfo P.
author_facet Marcos, Nelson
Azcarraga, Arnulfo P.
author_sort Marcos, Nelson
title Belief-evidence fusion in a hybrid intelligent system
title_short Belief-evidence fusion in a hybrid intelligent system
title_full Belief-evidence fusion in a hybrid intelligent system
title_fullStr Belief-evidence fusion in a hybrid intelligent system
title_full_unstemmed Belief-evidence fusion in a hybrid intelligent system
title_sort belief-evidence fusion in a hybrid intelligent system
publisher Animo Repository
publishDate 2004
url https://animorepository.dlsu.edu.ph/faculty_research/2156
_version_ 1751550432371539968