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Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (KPLAB - Knowledge Processing Lab)ORCID iD: 0000-0001-6356-045X
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Palo Alto: AAAI Press, 2019, p. 2760-2767Conference paper, Published paper (Refereed)
Abstract [en]

Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states represents the ‘true’ state. The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise progression under uncertainty. The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty.

Place, publisher, year, edition, pages
Palo Alto: AAAI Press, 2019. p. 2760-2767
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-153444ISI: 000485292602095OAI: oai:DiVA.org:liu-153444DiVA, id: diva2:1271459
Conference
AAAI Conference on Artificial Intelligence (AAAI)
Funder
CUGS (National Graduate School in Computer Science)Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-10-24

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de Leng, DanielHeintz, Fredrik

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  • nn-NB
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Output format
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