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  • 1.
    Andersson, Olov
    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.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization2015In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) / [ed] Blai Bonet and Sven Koenig, AAAI Press, 2015, p. 2497-2503Conference paper (Refereed)
    Abstract [en]

    Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

  • 2.
    Andersson, Olov
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. 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.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. 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.
    Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance2018In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper (Refereed)
    Abstract [en]

    A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

  • 3.
    Berglund, Aseel
    et al.
    Linköping University, Department of Computer and Information Science, Software and 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.
    Integrating Soft Skills into Engineering Education for Increased Student Throughput and more Professional Engineers2014In: Proceedings of LTHs 8:e Pedagogiska Inspirationskonferens (PIK), Lund, Sweden: Lunds university , 2014Conference paper (Other academic)
    Abstract [en]

    Soft skills are recognized as crucial for engineers as technical work is becoming more and more collaborative and interdisciplinary. Today many engineering educations fail to give appropriate training in soft skills. Linköping University has therefore developed a completely new course “Professionalism for Engineers” for two of its 5-year engineering programs in the area of computer science. The course stretches over the first 3 years with students from the three years taking it together. The purpose of the course is to give engineering students training in soft skills that are of importance during the engineering education as well as during their professional career. The examination is based on the Dialogue Seminar Method developed for learning from experience and through reflection. The organization of the course is innovative in many ways.

  • 4.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

    In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

  • 5.
    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.

  • 6.
    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).

  • 7.
    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).

  • 8.
    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.

  • 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.
    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.

  • 10.
    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.

  • 11. De Raedt, Luc
    et al.
    Bessiere, ChristianDubois, DidierDoherty, PatrickLinköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.Frasconi, PaoloHeintz, FredrikLinköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.Lucas, Peter
    Proceedings of the 20th European Conference on Artificial Intelligence (ECAI)2012Conference proceedings (editor) (Refereed)
  • 12.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Heintz, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Merz, Torsten
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Nyblom, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Persson, Tommy
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Wingman, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation2004In: 7th International Symposium on Distributed Autonomous Robotic Systems,2004, Toulouse: LAAS , 2004, p. 221-Conference paper (Refereed)
  • 13.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A Delegation-Based Cooperative Robotic Framework2011In: Proceedings of the IEEE International Conference on Robotics and Biomimetic, IEEE conference proceedings, 2011, p. 2955-2962Conference paper (Refereed)
    Abstract [en]

    Cooperative robotic systems, such as unmanned aircraft systems, are becoming technologically mature enough to be integrated into civil society. To gain practical use and acceptance, a verifiable, principled and well-defined foundation for interactions between human operators and autonomous systems is needed. In this paper, we propose and specify such a formally grounded collaboration framework. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint solving. The system is implemented as a prototype on unmanned aerial vehicle systems and a case study targeting emergency service applications is presented.

  • 14.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Delegation-Based Collaboration2012In: Proceedings of the 5th International Conference on Cognitive Systems (CogSys), 2012Conference paper (Other academic)
  • 15.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    High-level Mission Specification and Planning for Collaborative Unmanned Aircraft Systems using Delegation2013In: Unmanned Systems, ISSN 2301-3850, E-ISSN 2301-3869, Vol. 1, no 1, p. 75-119Article in journal (Refereed)
    Abstract [en]

    Automated specification, generation and execution  of high level missions involving one or more heterogeneous unmanned aircraft systems is in its infancy. Much previous effort has been focused on the development of air vehicle platforms themselves together with the avionics and sensor subsystems that implement basic navigational skills. In order to increase the degree of autonomy in such systems so they can successfully participate in more complex mission scenarios such as those considered in emergency rescue that also include ongoing interactions with human operators, new architectural components and functionalities will be required to aid not only human operators in mission planning, but also the unmanned aircraft systems themselves in the automatic generation, execution and partial verification of mission plans to achieve mission goals. This article proposes a formal framework and architecture based on the unifying concept of delegation that can be used for the automated specification, generation and execution of high-level collaborative missions involving one or more air vehicles platforms and human operators. We describe an agent-based software architecture, a temporal logic based mission specification language, a distributed temporal planner and  a task specification language that when integrated provide a basis for the generation, instantiation and execution of complex collaborative missions on heterogeneous air vehicle systems. A prototype of the framework is operational in a number of autonomous unmanned aircraft systems developed in our research lab.

  • 16.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Robotics, Temporal Logic and Stream Reasoning2013In: Proceedings of Logic for Programming Artificial Intelligence and Reasoning (LPAR), 2013, 2013Conference paper (Refereed)
  • 17.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Landén, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A Delegation-Based Architecture for Collaborative Robotics2011In: Agent-Oriented Software Engineering XI: 11th International Workshop, AOSE 2010, Toronto, Canada, May 10-11, 2010, Revised Selected Papers / [ed] Danny Weyns and Marie-Pierre Gleizes, Springer Berlin/Heidelberg, 2011, p. 205-247Chapter in book (Refereed)
    Abstract [en]

    Collaborative robotic systems have much to gain by leveraging results from the area of multi-agent systems and in particular agent-oriented software engineering. Agent-oriented software engineering has much to gain by using collaborative robotic systems as a testbed. In this article, we propose and specify a formally grounded generic collaborative system shell for robotic systems and human operated ground control systems. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process implemented in the collaborative system shell. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint problem solving. The system is implemented as a prototype on Unmanned Aerial Vehicle systems and a case study targeting emergency service applications is presented.

  • 18.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Landén, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A Delegation-Based Collaborative Robotic Framework2011In: Proceedings of the 3rd International Workshop on Collaborative Agents - Research and development / [ed] Christian Guttmann, 2011Conference paper (Refereed)
  • 19.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Landén, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A Distributed Task Specification Language for Mixed-Initiative Delegation2012In: Principles and Practice of Multi-Agent Systems: 13th International Conference, PRIMA 2010, Kolkata, India, November 12-15, 2010, Revised Selected Papers / [ed] Nirmit Desai, Alan Liu, Michael Winikoff, Springer Berlin/Heidelberg, 2012, Vol. 7057, p. 42-57Conference paper (Refereed)
    Abstract [en]

    In the next decades, practically viable robotic/agent systems are going to be mixed-initiative in nature. Humans will request help from such systems and such systems will request help from humans in achieving the complex mission tasks required. Pragmatically, one requires a distributed task specification language to define tasks and a suitable data structure which satisfies the specification and can be used flexibly by collaborative multi-agent/robotic systems. This paper defines such a task specification language and an abstract data structure called Task Specification Trees which has many of the requisite properties required for mixed-initiative problem solving and adjustable autonomy in a distributed context. A prototype system has been implemented for this delegation framework and has been used practically with collaborative unmanned aircraft systems.

  • 20.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A Temporal Logic-based Planning and Execution Monitoring Framework for Unmanned Aircraft Systems2009In: Autonomous Agents and Multi-Agent Systems, ISSN 1387-2532, E-ISSN 1573-7454, Vol. 19, no 3, p. 332-377Article in journal (Refereed)
    Abstract [en]

    Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.

  • 21.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Landén, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Olsson, Per-Magnus
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Research with Collaborative Unmanned Aircraft Systems2010In: Proceedings of the Dagstuhl Workshop on Cognitive Robotics / [ed] Gerhard Lakemeyer, Hector J. Levesque, Fiora Pirri, Leibniz-Zentrum für Informatik , 2010Conference paper (Refereed)
    Abstract [en]

    We provide an overview of ongoing research which targets development of a principled framework for mixed-initiative interaction with unmanned aircraft systems (UAS). UASs are now becoming technologically mature enough to be integrated into civil society. Principled interaction between UASs and human resources is an essential component in their future uses in complex emergency services or bluelight scenarios. In our current research, we have targeted a triad of fundamental, interdependent conceptual issues: delegation, mixed- initiative interaction and adjustable autonomy, that is being used as a basis for developing a principled and well-defined framework for interaction. This can be used to clarify, validate and verify different types of interaction between human operators and UAS systems both theoretically and practically in UAS experimentation with our deployed platforms.

  • 22.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    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.
    Conte, Gianpaolo
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems2014In: Handbook of Unmanned Aerial Vehicles / [ed] Kimon P. Valavanis, George J. Vachtsevanos, Dordrecht: Springer Science+Business Media B.V., 2014, p. 849-952Chapter in book (Other academic)
    Abstract [en]

    This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy.  A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework.  The architecture has been empirically tested in several complex missions, some of which are described in the chapter.

  • 23.
    Färnqvist, Tommy
    et al.
    Linköping University, Department of Computer and Information Science, Software and 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.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Mannila, Linda
    Åbo Academy, Finland.
    Wang, Chunyan
    Linköping University, Department of Computer and Information Science.
    Supporting Active Learning by Introducing an Interactive Teaching Tool in a Data Structures and Algorithms Course2016In: Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE 2016), ACM Publications, 2016, p. 663-668Conference paper (Refereed)
    Abstract [en]

    Traditionally, theoretical foundations in data structures and algorithms (DSA) courses have been covered through lectures followed by tutorials, where students practise their understanding on pen-and-paper tasks. In this paper, we present findings from a pilot study on using the interactive e-book OpenDSA as the main material in a DSA course. The goal was to redesign an already existing course by building on active learning and continuous examination through the use of OpenDSA. In addition to presenting the study setting, we describe findings from four data sources: final exam, OpenDSA log data, pre and post questionnaires as well as an observation study. The results indicate that students performed better on the exam than during previous years. Students preferred OpenDSA over traditional textbooks and worked actively with the material, although a large proportion of them put off the work until the due date approaches.

  • 24.
    Färnqvist, Tommy
    et al.
    Linköping University, Department of Computer and Information Science, Software and 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.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Mannila, Linda
    Åbo Academy, Finland.
    Wang, Chunyan
    Linköping University, Department of Computer and Information Science.
    Supporting Active Learning Using an Interactive Teaching Tool in a Data Structures and Algorithms Course2015In: Proceedings of 5:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng), 2015, p. 76-79Conference paper (Other academic)
    Abstract [en]

    Traditionally, theoretical foundations in data structuresand algorithms (DSA) courses have been covered throughlectures followed by tutorials, where students practise theirunderstanding on pen-and-paper tasks. In this paper, we presentfindings from a pilot study on using the interactive e-bookOpenDSA as the main material in a DSA course. The goal was toredesign an already existing course by building on active learningand continuous examination through the use of OpenDSA. Inaddition to presenting the study setting, we describe findings fromfour data sources: final exam, OpenDSA log data, pre- and postcourse questionnaires as well as an observation study. The resultsindicate that students performed better on the exam than duringprevious years. Students preferred OpenDSA over traditionaltextbooks and worked actively with the material, although alarge proportion of them put off the work until the due dateapproaches.

  • 25.
    Grimsberg, Michaël
    et al.
    LTH.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Kann, Viggo
    KTH.
    Erlander Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Öhrström, Lars
    Chalmers.
    Vem styr egentligen grundutbildningen?2015In: Proceedings of 5:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng), 2015Conference paper (Refereed)
    Abstract [sv]

    Vi belyser olikheter och likheter i hur grundutbildningen styrs på fyra svenska tekniska högskolor. Vi jämför hur lärare och examinatorer väljs ut, hur medel fördelas och vilken roll programansvariga (eller motsvarande) har. De strukturella skillnaderna är relativt stora med störst autonomi för programansvariga på Chalmers tekniska högskola vilket delvis har att göra med att detta lärosäte lyder under aktiebolagslagen.

  • 26.
    Heintz, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    DyKnow: A Stream-Based Knowledge Processing Middleware Framework2009Doctoral thesis, monograph (Other academic)
    Abstract [en]

    As robotic systems become more and more advanced the need to integrate existing deliberative functionalities such as chronicle recognition, motion planning, task planning, and execution monitoring increases. To integrate such functionalities into a coherent system it is necessary to reconcile the different formalisms used by the functionalities to represent information and knowledge about the world. To construct and integrate these representations and maintain a correlation between them and the environment it is necessary to extract information and knowledge from data collected by sensors. However, deliberative functionalities tend to assume symbolic and crisp knowledge about the current state of the world while the information extracted from sensors often is noisy and incomplete quantitative data on a much lower level of abstraction. There is a wide gap between the information about the world normally acquired through sensing and the information that is assumed to be available for reasoning about the world.

    As physical autonomous systems grow in scope and complexity, bridging the gap in an ad-hoc manner becomes impractical and inefficient. Instead a principled and systematic approach to closing the sensereasoning gap is needed. At the same time, a systematic solution has to be sufficiently flexible to accommodate a wide range of components with highly varying demands. We therefore introduce the concept of knowledge processing middleware for a principled and systematic software framework for bridging the gap between sensing and reasoning in a physical agent. A set of requirements that all such middleware should satisfy is also described.

    A stream-based knowledge processing middleware framework called DyKnow is then presented. Due to the need for incremental refinement of information at different levels of abstraction, computations and processes within the stream-based knowledge processing framework are modeled as active and sustained knowledge processes working on and producing streams. DyKnow supports the generation of partial and context dependent stream-based representations of past, current, and potential future states at many levels of abstraction in a timely manner.

    To show the versatility and utility of DyKnow two symbolic reasoning engines are integrated into Dy-Know. The first reasoning engine is a metric temporal logical progression engine. Its integration is made possible by extending DyKnow with a state generation mechanism to generate state sequences over which temporal logical formulas can be progressed. The second reasoning engine is a chronicle recognition engine for recognizing complex events such as traffic situations. The integration is facilitated by extending DyKnow with support for anchoring symbolic object identifiers to sensor data in order to collect information about physical objects using the available sensors. By integrating these reasoning engines into DyKnow, they can be used by any knowledge processing application. Each integration therefore extends the capability of DyKnow and increases its applicability.

    To show that DyKnow also has a potential for multi-agent knowledge processing, an extension is presented which allows agents to federate parts of their local DyKnow instances to share information and knowledge.

    Finally, it is shown how DyKnow provides support for the functionalities on the different levels in the JDL Data Fusion Model, which is the de facto standard functional model for fusion applications. The focus is not on individual fusion techniques, but rather on an infrastructure that permits the use of many different fusion techniques in a unified framework.

    The main conclusion of this thesis is that the DyKnow knowledge processing middleware framework provides appropriate support for bridging the sense-reasoning gap in a physical agent. This conclusion is drawn from the fact that DyKnow has successfully been used to integrate different reasoning engines into complex unmanned aerial vehicle (UAV) applications and that it satisfies all the stated requirements for knowledge processing middleware to a significant degree.

  • 27.
    Heintz, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    FCFoo992000In: Proceedings of RoboCup-99: Robot Soccer World Cup III (RoboCup), Springer London, 2000, Vol. 1856, p. 563-566Conference paper (Refereed)
    Abstract [en]

    Introduction The emphasis of FCFoo was mainly on building a library for developers of RoboCup teams, designed especially for educational use. After the competition the library was more or less totally rewritten and nally published as part of the Master Thesis of Fredrik Heintz [4]. The agents are built on a layered reactive-deliberative architecture. The four layers describes the agent on dierent levels of abstraction and deliberation. The lowest level is mainly reactive while the others are more deliberate. The teamwork is based on nite automatas and roles. A role is a set of attributes describing some of the behaviour of a player. The decision-making uses decisiontrees to classify the situation and select the appropriate skill to perform. The other two layers are used to calculate the actual command to be sent to the server. The agent architecture and the basic design are inspired by the champions of RoboCup'98, CMUnited [6, 7]. The idea of using decision-trees and role

  • 28.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Semantically Grounded Stream Reasoning Integrated with ROS2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2013, p. 5935-5942Conference paper (Refereed)
    Abstract [en]

    High level reasoning is becoming essential to autonomous systems such as robots. Both the information available to and the reasoning required for such autonomous systems is fundamentally incremental in nature. A stream is a flow of incrementally available  information and reasoning over streams is called stream reasoning.  Incremental reasoning over streaming information is  necessary to support a number of important robotics functionalities such as situation awareness, execution monitoring, and decision making.

    This paper presents a practical framework for semantically grounded temporal stream reasoning called DyKnow.  Incremental reasoning over  streams is achieved through efficient progression of temporal logical formulas. The reasoning is semantically grounded through a common ontology and a specification of the semantic content of streams relative to the ontology.  This allows the finding of relevant streams through semantic matching. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning framework is integrated in the Robot  Operating System (ROS) thereby extending it with a stream reasoning capability.

  • 29.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Berglund, Aseel
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Hedin, Björn
    KTH, Stockholm, Sweden.
    Kann, Viggo
    KTH, Stockholm, Sweden.
    En jämförelse mellan programsamanhållande kurser vid KTH och LiU2015In: Proceedings of 5:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng), 2015Conference paper (Other academic)
    Abstract [sv]

    Programsammanhållande kurser där studenter från årskurs 1-3 gemensamt reflekterar över teman med koppling till deras studier och framtida yrkesliv finns på både KTH och Linköpings universitet (LiU). Syftet med kurserna är främst att skapa en helhet i utbildningen och ge förståelse för vad den leder till, genom att få studenterna att reflektera över sina studier och sin kommande yrkesroll. Detta leder förhoppningsvis till ökad genomströmning och minskade avhopp. Kurserna har gemensamt ursprung men har utvecklats i olika riktningar. Artikeln jämför tre programsammanhållande kurser för Datateknik KTH, Medieteknik KTH samt Data- och mjukvaruteknik Linköpings universitet.

  • 30.
    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.

  • 31.
    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.

  • 32.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    A knowledge processing middleware framework and its relation to the JDL data fusion model2006In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 17, no 4, p. 335-351Article in journal (Refereed)
    Abstract [en]

    Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose successful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a knowledge processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the JDL data fusion model.

  • 33.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    A knowledge processing middleware framework and its relation to the JDL data fusion model.2005In: The 8th International Conference on Information Fusion,2005, 2005Conference paper (Refereed)
    Abstract [en]

      

  • 34.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    A Knowledge processing Middleware Framework and its Relation to the JDL Data Fusion model2005In: 3rd joint SAIS-SSL event on Artificial Intelligence an Learning Systems,2005, Västerås: Mälardalens University , 2005, p. 68-Conference paper (Refereed)
  • 35.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    A Knowledge processing Middleware Framework and its Relation to the JDL Data Fusion model2005In: SWAR 05,2005, 2005, p. 50-51Conference paper (Other academic)
  • 36.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Chronicle Recognition in the WITAS UAV Project: A Preliminary Report2001In: Proceedings of the Swedish AI Society Workshop, 2001Conference paper (Refereed)
    Abstract [en]

    This paper describes the chronicle recognition problem and reports its status in the WITAS UAV project. We describe how we use the IxTeT chronicle recognition system to define chronicles (scenarios or situations), like a vehicle passing another vehicle, and how it is incorporated in the WITAS architecture. We also discuss known problems with the current system and possible directions of future research.

  • 37.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    DyKnow: A Framework for Processing Dynamic Knowledge and Object Structures in Autonomous Systems2004In: Proceedings of the Second Joint SAIS/SSLS Workshop, 2004Conference paper (Refereed)
  • 38.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    DyKnow: A Framework for Processing Dynamic Knowledge and Object Structures in Autonomous Systems2004In: Proceedings of the International Workshop on Monitoring, Security, and Rescue Techniques in Multi-Agent Systems (MSRAS) / [ed] Barbara Dunin-Keplicz, Andrzej Jankowski, Andrzej Skowron, Marcin Szczuka, Springer, 2004, p. 479-492Conference paper (Refereed)
    Abstract [en]

    Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agent’s embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.

  • 39.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    DyKnow: An approach to middleware for knowledge processing2004In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 15, no 1, p. 3-13Article in journal (Refereed)
    Abstract [en]

    Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.

  • 40.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    DyKnow Federations: Distributing and Merging Information Among UAVs2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION), IEEE conference proceedings , 2008Conference paper (Refereed)
    Abstract [en]

    As unmanned aerial vehicle (UAV) applications become more complex and versatile there is an increasing need to allow multiple UAVs to cooperate to solve problems which are beyond the capability of each individual UAV. To provide more complete and accurate information about the environment we present a DyKnow federation framework for information integration in multi-node networks of UAVs. A federation is created and maintained using a multiagent delegation framework and allows UAVs to share local information as well as process information from other UAVs as if it were local using the DyKnow knowledge processing middleware framework. The work is presented in the context of a multi UAV traffic monitoring scenario.

  • 41.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Federated DyKnow, a Distributed Information Fusion System for Collaborative UAVs2010In: Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE conference proceedings, 2010, p. 1063-1069Conference paper (Refereed)
    Abstract [en]

    As unmanned aerial vehicle (UAV) applications are becoming more complex and covering larger physical areas there is an increasing need for multiple UAVs to cooperatively solve problems. To produce more complete and accurate information about the environment we present the DyKnow Federation framework for distributed fusion among collaborative UAVs. A federation is created and maintained using a multi-agent delegation framework which allows high-level specification and reasoning about resource bounded cooperative problem solving. When the federation is set up, local information is transparently shared between the agents according to specification. The work is presented in the context of a multi-UAV traffic monitoring scenario.

  • 42.
    Heintz, Fredrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Managing Dynamic Object Structures using Hypothesis Generation and Validation2004In: AAAI Workshop on Anchoring Symbols to Sensor Data,2004, Menlo Park, California: AAAI Press , 2004, p. 54-62Conference paper (Refereed)
  • 43.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Dragisic, Zlatan
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Semantic Information Integration for Stream Reasoning2012In: Proceedings of the 15th International Conference on Information Fusion (FUSION), Linköping: Linköping University Electronic Press, 2012Conference paper (Other academic)
    Abstract [en]

    The main contribution of this paper is a practicalsemantic information integration approach for stream reasoningbased on semantic matching. This is an important functionality for situation awareness applications where temporal reasoning over streams from distributed sources is needed. The integration is achieved by creating a common ontology, specifying the semantic content of streams relative to the ontology and then use 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.

  • 44.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Erlander Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Civilingenjör i Mjukvaruteknik vid Linköpings universitet: mål, design och erfarenheter2013In: Proceedings of 4:de Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng) / [ed] S. Vikström, R. Andersson, F. Georgsson, S. Gunnarsson, J. Malmqvist, S. Pålsson och D. Raudberget, 2013Conference paper (Refereed)
    Abstract [en]

    Hösten 2013 startade Linköpings universitet den första civilingenjörsutbildningen i Mjukvaruteknik. Utbildningens mål är att bland annat att ge ett helhetsperspektiv på modern storskalig mjukvaruutveckling, ge en gedigen grund i datavetenskap och computational thinking samt främja entreprenörskap och innovation. Studenternas gensvar har varit över förväntan med över 600 sökande till de 30 platserna varav 134 förstahandssökande. Här presenterar vi programmets vision, mål, designprinciper samt det färdiga programmet. En viktig förebild är ACM/IEEE Computer Science Curricula som precis kommit i en ny uppdaterad version. Tre pedagogiska idéer vi har följt är (1) att använda projektkurser för att integrera teori och praktik samt ge erfarenhet i den vanligaste arbetsformen i näringslivet; (2) att undervisa i flera olika programspråk och flera olika programutvecklingsmetodiker för att ge en plattform att ta till sig det senaste på området; och (3) att införa en programsammanhållande kurs i ingenjörsprofessionalism i årskurs 1–3 som ger studenterna verktyg att reflektera över sitt eget lärande, att jobba i näringslivet samt sin professionella yrkesroll. Artikeln avslutas med en diskussion om viktiga aspekter som computational thinking och ACM/IEEE CS Curricula.

  • 45.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Erlander Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    The Design of Sweden's First 5-year Computer Science and Software Engineering Program2014In: Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE 2014), ACM Press, 2014, p. 199-204Conference paper (Refereed)
  • 46.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Färnqvist, Tommy
    Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory. Linköping University, The Institute of Technology.
    Pedagogical Experiences of Competitive Elements in an Algorithms Course2012In: Proceedings of LTHs 7:e Pedagogiska Inspirationskonferens (PIK), 2012Conference paper (Refereed)
    Abstract [en]

    We claim that competitive elements can improve thequality of programming and algorithms courses. To test this, weused our experience from organising national and internationalprogramming competitions to design and evaluate two differentcontests in an introductory algorithms course. The first contestturned lab assignments into a competition, where two groups rancompetitions and two were control groups and did not compete.The second, voluntary, contest, consisting of 15 internationalprogramming competition style problems, was designed tosupport student skill acquisition by providing them withopportunities for deliberate practise. We found that competitiveelements do influence student behaviour and our mainconclusions from the experiment are that students really likecompetitions, that the competition design is very important forthe resulting behaviour of the students, and that active studentsperform better on exams.

    We also report on an extra-curricular activity in the form of asemester long programming competition as a way of supportingstudent's deliberate practise in computer programming.

  • 47.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Färnqvist, Tommy
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology.
    Återkoppling genom automaträttning2013In: Proceedings of 4:de Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng), 2013Conference paper (Refereed)
    Abstract [sv]

    Vi har undersökt olika former av återkoppling genom automaträttning i en kurs i datastrukturer och algoritmer. 2011 undersökte vi effekterna av tävlingsliknande moment som också använder automaträttning. 2012 införde vi automaträttning av laborationerna. Vi undersökte då hur återkoppling genom automaträttning påverkar studenternasarbetssätt, prestationsgrad och relation till den examinerande personalen. Genom automaträttning får studenterna omedelbar återkoppling om deras program är tillräckligt snabbt och ger rätt svar på testdata. När programmet är korrekt och resurseffektivt kontrollerar kursassistenterna att programmet även uppfyller andra krav som att vara välskrivet och välstrukturerat. Efter kursen undersökte vi studenternas inställning till och upplevelse av automaträttning genom en enkät. Resultaten är att studenterna är positiva till automaträttning (80% av alla som svarade) och att den påverkade studenternas sätt att arbeta huvudsakligen positivt. Till exempel svarade 50% att de ansträngde sig hårdare tack vare automaträttningen. Dessutom blir rättningen mer objektiv då den görs på exakt samma sätt för alla. Vår slutsats är att återkoppling genom automaträttning ger positiva effekter och upplevs som positiv av studenterna.

  • 48.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Färnqvist, Tommy
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Thorén, Jesper
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, Faculty of Science & Engineering.
    Programutvecklingsstrategier för att öka kopplingen mellan programmering och matematik2015In: Proceedings of 5:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar (UtvSvIng), 2015Conference paper (Refereed)
    Abstract [sv]

    Matematik och programmering är två viktiga inslag i civilingenjörsprogram inom data- och mjukvaruteknik. De studenter som klarar dessa kurser klarar sannolikt resten av utbildningen. Idag har fler studenter programmering än matematik som huvudsakligt intresse. Därför har Linköpings universitet aktivt jobbat med olika strategier för att öka kopplingen mellan programmering och matematik, främst i de inledande kurserna. För att undersöka studenternas attityder till matematik och programmering har vi genomfört flera enkätstudier som bl.a. visar att intresset för matematik är stort men intresset för programmering ännu större och att studenterna tror de kommer ha betydligt mer nytta av programmering än matematik under sin karriär. Texten är tänkt som grund för en diskussion kring hur kopplingarna mellan matematik och programmering kan göras tydligare och starkare.

  • 49.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Roll, Jacob
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    FlexDx: A Reconfigurable Diagnosis Framework2008In: Proceedings of the 19th International Workshop on Principles of Diagnosis (DX), 2008Conference paper (Refereed)
    Abstract [en]

    Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper describes FlexDx, a reconfigurable diagnosis framework which reduces the computational burden by only running the tests that are currently needed. The method selects tests such that the isolation performance of the diagnostic system is maintained. Special attention is given to the practical issues introduced by a reconfigurable diagnosis framework such as FlexDx. For example, tests are added and removed dynamically, tests are partially performed on historic data, and synchronous and asynchronous processing are combined. To handle these issues FlexDx uses DyKnow, a stream-based knowledge processing middleware framework. The approach is exemplified on a relatively small dynamical system, which still illustrates the computational gain with the proposed approach.

  • 50.
    Heintz, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Kummeneje, Johan
    Stockholm University.
    Scerri, Paul
    Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
    Simulated RoboCup in University Undergraduate Education2000In: Proceedings of the Fourth Internation Workshop on RoboCup, Springer Berlin/Heidelberg, 2000, Vol. 2019, p. 309-314Conference paper (Refereed)
    Abstract [en]

    We argue that RoboCup can be used to improve the teaching of AI in undergraduate education. We give some examples of how AI courses using RoboCup can be implemented using a problem based approach at two different Universities. To reduce the negative aspects found we present a solution, with the aim of easing the burden of grasping the domain of RoboCup for the students, RoboSoc which is a general framework for developing simulated RoboCup agents.

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