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Robust Stream Reasoning 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
2019 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem.

Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement.

The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. , p. 207
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2006
Keywords [en]
stream reasoning, stream processing, temporal reasoning, spatial reasoning, configuration planning, intelligent robotics
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-157633DOI: 10.3384/diss.diva-157633ISBN: 9789176850138 (print)OAI: oai:DiVA.org:liu-157633DiVA, id: diva2:1367625
Public defence
2019-12-06, Ada Lovelace, Hus B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
CUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research , CUASSwedish Research Council, CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2019-11-08 Created: 2019-11-04 Last updated: 2019-11-08Bibliographically approved

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CiteExportLink to record
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