Jia-Hong Wu, Robert Givan
We consider how to learn useful relational features in linear approximated value function representations for solving probabilistic planning problems. We first discuss a current feature-discovering planner that we presented at the International Conference on Automated Planning and Scheduling (ICAPS) in 2007. We then propose how the feature learning framework can be further enhanced to improve problem solving ability.
Subjects: 12. Machine Learning and Discovery; 11. Knowledge Representation
Submitted: Sep 11, 2007