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.
|Original language||American English|
|Number of pages||7|
|State||Published - 1 Jan 2014|
|Event||AAAI Spring Symposium - Technical Report - |
Duration: 1 Jan 2014 → …
|Conference||AAAI Spring Symposium - Technical Report|
|Period||1/01/14 → …|