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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rrqbmymjsrc75j6bbyyyprde5e" style="color: black;">Proceedings of the 6th International Conference on Agents and Artificial Intelligence</a>
Being able to predict events and occurrences which may arise from a current situation is a desirable capability of an intelligent agent. In this paper, we show that a high-level scene interpretation system, implemented as part of a comprehensive robotic system in the RACE project, can also be used for prediction. This way, the robot can foresee possible developments of the environment and the effect they may have on its activities. As a guiding example, we consider a robot acting as a waiter in<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5220/0004819704690476">doi:10.5220/0004819704690476</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icaart/LehmannNBH14.html">dblp:conf/icaart/LehmannNBH14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vjwjqdezgrddxclzkxr64nlgne">fatcat:vjwjqdezgrddxclzkxr64nlgne</a> </span>
more »... a restaurant and the task of predicting possible occurrences and courses of action, e.g. when serving a coffee to a guest. Our approach requires that the robot possesses conceptual knowledge about occurrences in the restaurant and its own activities, represented in the standardized ontology language OWL and augmented by constraints using SWRL. Conceptual knowledge may be acquired by conceptualizing experiences collected in the robot's memory. Predictions are generated by a model-construction process which seeks to explain evidence as parts of such conceptual knowledge, this way generating possible future developments. The experimental results show, among others, the prediction of possible obstacle situations and their effect on the robot actions and estimated execution times.
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