Decreasing Uncertainty in Planning with State Prediction

Decreasing Uncertainty in Planning with State Prediction

Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sandor Szedmak, Justus Piater

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2032-2038. https://doi.org/10.24963/ijcai.2017/282

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 machine-learning 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.
Keywords:
Machine Learning: Semi-Supervised Learning
Planning and Scheduling: Planning under Uncertainty
Planning and Scheduling: Robot Planning
Uncertainty in AI: Uncertainty in AI