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Auflistung nach Autor:in "Pecora, Federico"

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  • Zeitschriftenartikel
    Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Pecora, Federico
    Researchers in AI and Robotics have in common the desire to “make robots intelligent”, evidence of which can be traced back to the earliest AI systems. One major contribution of AI to Robotics is the model-centered approach, whereby intelligence is the result of reasoning in models of the world which can be changed to suit different environments, physical capabilities, and tasks. Dually, robots have contributed to the formulation and resolution of challenging issues in AI, and are constantly eroding the modeling abstractions underlying AI problem solving techniques. Forty-eight years after the first AI-driven robot, this article provides an updated perspective on the successes and challenges which lie at the intersection of AI and Robotics.
  • Zeitschriftenartikel
    Special Issue on Reintegrating Artificial Intelligence and Robotics
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Pecora, Federico; Mansouri, Masoumeh; Hawes, Nick; Kunze, Lars
  • 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.