Integrated model for informal inference based on neural networks

Kyung Joong Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Inference is one of human's high-level functionalities and it is not easy to implement in machine. It is believed that inference is not results of single neuron's activity. Instead, it is a complex activity generated by multiple neural networks. Unlike computer, it is more flexible and concludes differently even for the similar situations in case of human. In this paper, these characteristics are defined as "informality." Informality in inference can be implemented using the interaction of multiple neural networks with the inclusion of internal or subjective properties. Simple inference tasks such as pattern recognition and robot control are solved based on the informal inference ideas. Especially, fuzzy integral and behavior network methods are adopted to realize that. Experimental results show that the informal inference can perform better with more flexibility compared to the previous static approaches.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Number of pages10
EditionPART 2
Publication statusPublished - 2008
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 2007 Nov 132007 Nov 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Neural Information Processing, ICONIP 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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