Some Active Learning Schemes to Acquire Control Knowledge for Planning

Active learning consists on the incremental generation of “good” training examples for machine learning techniques. Usually, the objective is to balance between cost of generating and analyzing all the instance space, and cost of generation of “good” examples. While there has been some work on its application to inductive learning techniques, there have been very few approaches applying it to problem solving, and even less to task planning. In this paper, we present an on-going work on building some such schemes for acquiring control knowledge, in the form of control rules, for a planner. Results show that the scheme improves not only in terms of learning convergence, but also in terms of performance of the learned knowledge.