The problem of uniquely identifying an entity described in natural language, known as reference resolution, has become recognized as a critical problem for the field of robotics, as it is necessary in order for robots to be able to discuss, reason about, or perform actions involving any people, locations, or objects in their environments. However, most existing algorithms for reference resolution are domain-specific and limited to environments assumed to be known a priori. In this paper we present an algorithm for reference resolution which is both domain independent and designed to operate in an open world. We call this algorithm POWER: Probabilistic Open-World Entity Resolution. We then present the results of an empirical study demonstrating the success of POWER both in properly identifying the referents of referential expressions and in properly modifying the world model based on such expressions.
@incollection{williamsscheutz15iros,
title={POWER: A Domain-Independent Algorithm for Probabilistic, Open-World Entity Resolution},
author={Tom Williams and Matthias Scheutz},
year={2015},
booktitle={Proceedings of IROS},
url={https://hrilab.tufts.edu/publications/williamsscheutz15iros.pdf}
doi={10.1109/IROS.2015.7353526}
}