Decreasing uncertainty in planning with state prediction

Tools

Krivic, Senka and Cashmore, Michael and Magazzeni, Daniele and Ridder, Bram and Szedmak, Sandor and Piater, Justus; Sierra, Carles, ed. (2017) Decreasing uncertainty in planning with state prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI-17. IJCAI, AUS, pp. 2032-2038. ISBN 9780999241103 (https://doi.org/10.24963/ijcai.2017/282)

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Abstract

In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machinelearning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.

ORCID iDs

Krivic, Senka, Cashmore, Michael ORCID logoORCID: https://orcid.org/0000-0002-8334-4348, Magazzeni, Daniele, Ridder, Bram, Szedmak, Sandor and Piater, Justus; Sierra, Carles
CORE (COnnecting REpositories)