Probabilistic logic for multi-robot event recognition

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationQualitative Representations for Robots
Subtitle of host publicationPapers from the AAAI Spring Symposium, Technical Report
PublisherAI Access Foundation
Number of pages7
ISBN (Print)9781577356462
Publication statusPublished - Mar 2014
Externally publishedYes
Event2014 AAAI Spring Symposium - Palo Alto, CA, United States
Duration: 24 Mar 201426 Mar 2014

Publication series

NameAAAI Spring Symposium - Technical Report


Conference2014 AAAI Spring Symposium
Country/TerritoryUnited States
CityPalo Alto, CA


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