Probabilistic logic for multi-robot event recognition

José A. Gurzoni, Paulo E. Santos, Murilo F. Martins, Fabio G. Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

Resumen

This paper presents initial results towards the development of a logic-based probabilistic event recognition system capable of learning and inferring high-level joint actions from simultaneous task execution demonstrations on a search and rescue scenario. We adopt the MLN-EC event recognition system, which extends probabilistically the Event Calculus using Markov Logic Networks, to learn and infer the intentions of the human operators teleoperating robots in a real-world robotic search and rescue task. Experimental results in both simulation and real robots show that the probabilistic event logic can recognise the actions taken by the human teleoperators in real world multi-robot domains, even with uncertain and noisy data. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.
Idioma originalInglés estadounidense
Páginas50-56
Número de páginas7
EstadoPublicada - 1 ene 2014
Publicado de forma externa
EventoAAAI Spring Symposium - Technical Report -
Duración: 1 ene 2014 → …

Conferencia

ConferenciaAAAI Spring Symposium - Technical Report
Período1/01/14 → …

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  • Citar esto

    Gurzoni, J. A., Santos, P. E., Martins, M. F., & Cozman, F. G. (2014). Probabilistic logic for multi-robot event recognition. 50-56. Papel presentado en AAAI Spring Symposium - Technical Report, .