Fuzzy qualitative behaviour prioritisation

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

1 Citation (Scopus)

Abstract

Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE)
Place of PublicationImperial College, London
Pages1-6
Number of pages6
Publication statusPublished - Jul 2007

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Large scale systems

Keywords

  • Computational efficiency
  • Context modeling
  • Educational institutions
  • Expert systems
  • Fuzzy reasoning
  • Fuzzy sets
  • Fuzzy systems
  • Inference algorithms
  • Knowledge Based systems
  • Set theory

Cite this

Coghill, G. M. (2007). Fuzzy qualitative behaviour prioritisation. In Proceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE) (pp. 1-6). Imperial College, London.

Fuzzy qualitative behaviour prioritisation. / Coghill, George MacLeod.

Proceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE). Imperial College, London, 2007. p. 1-6.

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

Coghill, GM 2007, Fuzzy qualitative behaviour prioritisation. in Proceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE). Imperial College, London, pp. 1-6.
Coghill GM. Fuzzy qualitative behaviour prioritisation. In Proceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE). Imperial College, London. 2007. p. 1-6
Coghill, George MacLeod. / Fuzzy qualitative behaviour prioritisation. Proceedings of the IEEE Conference on Fuzzy Systems (Fuzz-IEEE). Imperial College, London, 2007. pp. 1-6
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