On Efficient Evolving Multi-Context Systems

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

2 Citations (Scopus)

Abstract

Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formalism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case.
Original languageEnglish
Title of host publicationPRICAI 2014: Trends in Artificial Intelligence
Subtitle of host publication13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings
EditorsDN Pham, S Park
PublisherSpringer International Publishing
Pages284-296
ISBN (Electronic)978-3-319-13560-1
ISBN (Print)978-3-319-13559-5
DOIs
Publication statusPublished - 2014
EventPacific Rim International Conference on Artificial Intelligence (PRICAI) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer International Publishing
Volume8862
ISSN (Print)0302-9743

Conference

ConferencePacific Rim International Conference on Artificial Intelligence (PRICAI)
Period1/01/14 → …

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Polynomials

Cite this

Knorr, M., Gonçalves, R., & Leite, J. A. C. P. (2014). On Efficient Evolving Multi-Context Systems. In DN. Pham, & S. Park (Eds.), PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings (pp. 284-296). (Lecture Notes in Artificial Intelligence; Vol. 8862). Springer International Publishing. https://doi.org/10.1007/978-3-319-13560-1_23
Knorr, Matthias ; Gonçalves, Ricardo ; Leite, João Alexandre Carvalho Pinheiro. / On Efficient Evolving Multi-Context Systems. PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings. editor / DN Pham ; S Park. Springer International Publishing, 2014. pp. 284-296 (Lecture Notes in Artificial Intelligence).
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Knorr, M, Gonçalves, R & Leite, JACP 2014, On Efficient Evolving Multi-Context Systems. in DN Pham & S Park (eds), PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings. Lecture Notes in Artificial Intelligence, vol. 8862, Springer International Publishing, pp. 284-296, Pacific Rim International Conference on Artificial Intelligence (PRICAI), 1/01/14. https://doi.org/10.1007/978-3-319-13560-1_23

On Efficient Evolving Multi-Context Systems. / Knorr, Matthias; Gonçalves, Ricardo ; Leite, João Alexandre Carvalho Pinheiro.

PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings. ed. / DN Pham; S Park. Springer International Publishing, 2014. p. 284-296 (Lecture Notes in Artificial Intelligence; Vol. 8862).

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

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N2 - Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formalism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case.

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Knorr M, Gonçalves R, Leite JACP. On Efficient Evolving Multi-Context Systems. In Pham DN, Park S, editors, PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings. Springer International Publishing. 2014. p. 284-296. (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-319-13560-1_23