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Auflistung nach Autor:in "Stock, Sebastian"

1 - 3 von 3
  • Zeitschriftenartikel
    Hierarchical Hybrid Planning for Mobile Robots
    (KI - Künstliche Intelligenz: Vol. 31, No. 4, 2017) Stock, Sebastian
  • Zeitschriftenartikel
    The RACE Project
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Hertzberg, Joachim; Zhang, Jianwei; Zhang, Liwei; Rockel, Sebastian; Neumann, Bernd; Lehmann, Jos; Dubba, Krishna S. R.; Cohn, Anthony G.; Saffiotti, Alessandro; Pecora, Federico; Mansouri, Masoumeh; Konečný, Štefan; Günther, Martin; Stock, Sebastian; Lopes, Luis Seabra; Oliveira, Miguel; Lim, Gi Hyun; Kasaei, Hamidreza; Mokhtari, Vahid; Hotz, Lothar; Bohlken, Wilfried
    This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.
  • Konferenzbeitrag
    Towards a flexible hybrid planner for machine coordination in arable farming
    (Informatik in der Land-, Forst- und Ernährungswirtschaft 2016, 2016) Stock, Sebastian; Lingemann, Kai; Stiene, Stefan; Hertzberg, Joachim
    In this paper we propose a new approach to coordinate multiple machines in the grain harvesting process, based on meta-constraint reasoning. This way we obtain more flexible plans that can be adapted at execution time. As an example scenario we focus on silage maize harvesting. We argue that more sophisticated flexible planning mechanisms are needed in order to obtain flexible plans that can be adapted at runtime to changing parameters such as yield per area.