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 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
