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  • 1.
    de Leng, Daniel
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Querying Flying Robots and Other Things: Ontology-supported stream reasoning2015In: XRDS: Crossroads, The ACM Magazine for Students - The Internet of Things, ISSN 1528-4972, Vol. 22, no 2, p. 44-47Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    A discussion on the role of ontologies and stream reasoning in Internet of Things applications.

  • 2.
    de Leng, Daniel
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    A lot of today's data is generated incrementally over time by a large variety of 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, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its 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 thesis, we integrate techniques for logic-based spatio-temporal 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 streaming data and the problem of robustly managing streaming data and its refinement.

    The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique 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 in 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 the context of 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 spatio-temporal stream reasoning, even when the availability of streaming resources changes.

  • 3.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty2019In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Palo Alto: AAAI Press, 2019Conference 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.

  • 4.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, Faculty of Science & Engineering.
    Ontology-Based Introspection in Support of Stream Reasoning2015In: Thirteenth scandinavian conference on artificial intelligence (SCAI) / [ed] S. Nowaczyk, IOS Press, 2015, p. 78-87Conference paper (Other academic)
    Abstract [en]

    Building complex systems such as autonomous robots usually require the integration of a wide variety of components including high-level reasoning functionalities. One important challenge is integrating the information in a system by setting up the data flow between the components. This paper extends our earlier work on semantic matching with support for adaptive on-demand semantic information integration based on ontology-based introspection. We take two important standpoints. First, we consider streams of information, to handle the fact that information often becomes continually and incrementally available. Second, we explicitly represent the semantics of the components and the information that can be provided by them in an ontology. Based on the ontology our custom-made stream configuration planner automatically sets up the stream processing needed to generate the streams of information requested. Furthermore, subscribers are notified when properties of a stream changes, which allows them to adapt accordingly. Since the ontology represents both the systems information about the world and its internal stream processing many other powerful forms of introspection are also made possible. The proposed semantic matching functionality is part of the DyKnow stream reasoning framework and has been integrated in the Robot Operating System (ROS).

  • 5.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, Faculty of Science & Engineering.
    Ontology-Based Introspection in Support of Stream Reasoning2015In: Proceedings of the Joint Ontology Workshops (JOWO 2015), Buenos Aires, Argentina, July 25-27, 2015: The Joint Ontology Workshops - Episode 1 / [ed] Odile Papini, Salem Benferhat, Laurent Garcia, Marie-Laure Mugnier, Eduardo Fermé, Thomas Meyer, Renata Wassermann, Torsten Hahmann, Ken Baclawski, Adila Krisnadhi, Pavel Klinov, Stefano Borgo and Oliver Kutz Daniele Porello15, Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V , 2015, Vol. 1517, p. 1-8Conference paper (Other academic)
    Abstract [en]

    Building complex systems such as autonomous robots usually require the integration of a wide variety of components including high-level reasoning functionalities. One important challenge is integrating the information in a system by setting up the data flow between the components. This paper extends our earlier work on semantic matching with support for adaptive on-demand semantic information integration based on ontology-based introspection.  We take two important stand-points.  First, we consider streams of information, to handle the fact that information often becomes continually and incrementally available.  Second, we explicitly represent the semantics of the components and the information that can be provided by them in an ontology.  Based on the ontology our custom-made stream configuration planner automatically sets up the stream processing needed to generate the streams of information requested. Furthermore, subscribers are notified when properties of a stream changes, which allows them to adapt accordingly. Since the ontology represents both the system's information about the world and its internal stream processing many other powerful forms of introspection are also made possible. The proposed semantic matching functionality is part of the DyKnow stream reasoning framework and has been integrated in the Robot Operating System (ROS).

  • 6.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Partial-State Progression for Stream Reasoning with Metric Temporal Logic2018In: Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR) / [ed] Michael Thielscher, Francesca Toni, and Frank Wolter, Palo Alto: AAAI Press, 2018, p. 633-634Conference paper (Refereed)
    Abstract [en]

    The formula progression procedure for Metric Temporal Logic (MTL), originally proposed by Bacchus and Kabanza, makes use of syntactic formula rewritings to incrementally evaluate MTL 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 robot applications. Our main contribution is an extension of the progression procedure to handle partial state information. For each missing truth value, we efficiently consider all consistent hypotheses by branching progression for each such hypothesis. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise partial-state progression.

  • 7.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Qualitative Spatio-Temporal Stream Reasoning With Unobservable Intertemporal Spatial Relations Using Landmarks2016In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI) / [ed] Dale Schuurmans, Dale Wellman, AAAI Press, 2016, Vol. 2, p. 957-963Conference paper (Refereed)
    Abstract [en]

    Qualitative spatio-temporal reasoning is an active research area in Artificial Intelligence. In many situations there is a need to reason about intertemporal qualitative spatial relations, i.e. qualitative relations between spatial regions at different time-points. However, these relations can never be explicitly observed since they are between regions at different time-points. In applications where the qualitative spatial relations are partly acquired by for example a robotic system it is therefore necessary to infer these relations. This problem has, to the best of our knowledge, not been explicitly studied before. The contribution presented in this paper is two-fold. First, we present a spatio-temporal logic MSTL, which allows for spatio-temporal stream reasoning. Second, we define the concept of a landmark as a region that does not change between time-points and use these landmarks to infer qualitative spatio-temporal relations between non-landmark regions at different time-points. The qualitative spatial reasoning is done in RCC-8, but the approach is general and can be applied to any similar qualitative spatial formalism.

  • 8.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Towards On-Demand Semantic Event Processing for Stream Reasoning2014In: 17th International Conference on Information Fusion, 2014Conference paper (Other academic)
    Abstract [en]

    The ability to automatically, on-demand, apply pattern matching over streams of information to infer the occurrence of events is an important fusion functionality. Existing event detection approaches require explicit configuration of what events to detect and what streams to use as input. This paper discusses on-demand semantic event processing, and extends the semantic information integration approach used in the stream processing middleware framework DyKnow to incorporate this new feature. By supporting on-demand semantic event processing, systems can automatically configure what events to detect and what streams to use as input for the event detection. This can also include the detection of lower-level events as well as processing of streams. The semantic stream query language C-SPARQL is used to specify events, which can be seen as transformations over streams. Since semantic streams consist of RDF triples, we suggest a method to convert between RDF streams and DyKnow streams. DyKnow is integrated in the Robot Operating System (ROS) and used for example in collaborative unmanned aircraft systems missions.

  • 9.
    de Leng, Daniel
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Tiger, Mattias
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Almquist, Mathias
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Almquist, Viktor
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Carlsson, Niklas
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Second Screen Journey to the Cup: Twitter Dynamics during the Stanley Cup Playoffs2018In: Proceedings of the 2nd Network Traffic Measurement and Analysis Conference (TMA), 2018, p. 1-8Conference paper (Refereed)
    Abstract [en]

    With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.

  • 10.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    de Leng, Daniel
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology. Utrecht University, The Netherlands.
    Semantic Information Integration with Transformations for Stream Reasoning2013In: 16th International Conference on Information Fusion, IEEE , 2013, p. 445-452Conference paper (Refereed)
    Abstract [en]

    The automatic, on-demand, integration of information from multiple diverse sources outside the control of the application itself is central to many fusion applications. An important problem is to handle situations when the requested information is not directly available but has to be generated or adapted through transformations. This paper extends the semantic information integration approach used in the stream-based knowledge processing middleware DyKnow with support for finding and automatically applying transformations. Two types of transformations are considered. Automatic transformation between different units of measurements and between streams of different types. DyKnow achieves semantic integration by creating a common ontology, specifying the semantic content of streams relative to the ontology and using semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System (ROS) and used in collaborative unmanned aircraft systems missions.

  • 11.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    de Leng, Daniel
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Spatio-Temporal Stream Reasoning with Incomplete Spatial Information2014In: Proceedings of the Twenty-first European Conference on Artificial Intelligence (ECAI'14), August 18-22, 2014, Prague, Czech Republic / [ed] Torsten Schaub, Gerhard Friedrich and Barry O'Sullivan, IOS Press, 2014, p. 429-434Conference paper (Refereed)
    Abstract [en]

    Reasoning about time and space is essential for many applications, especially for robots and other autonomous systems that act in the real world and need to reason about it. In this paper we present a pragmatic approach to spatio-temporal stream reasoning integrated in the Robot Operating System through the DyKnow framework. The temporal reasoning is done in the Metric Temporal Logic and the spatial reasoning in the Region Connection Calculus RCC-8. Progression is used to evaluate spatio-temporal formulas over incrementally available streams of states. To handle incomplete information the underlying first-order logic is extended to a three-valued logic. When incomplete spatial information is received, the algebraic closure of the known information is computed. Since the algebraic closure might have to be re-computed every time step, we separate the spatial variables into static and dynamic variables and reuse the algebraic closure of the static variables, which reduces the time to compute the full algebraic closure. The end result is an efficient and useful approach to spatio-temporal reasoning over streaming information with incomplete information.

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