Most learning research concerns classification. Research in learning and
planning and problem solving focuses on improving the performance of an AI
planning or problem solving system through experience. Our work has focussed
on integrating
explanation-based learning (EBL) and inductive learning
(specifically
ILP) to improve the efficiency (speedup
learning) and solution-quality for planning and problem solving systems by
solving sample problems and learning heuristics that avoid backtracking or
sub-optimal solutions.
Our work has focused on two systems:
- SCOPE: Learning control rules for partial-order planning to improve
efficiency and plan quality
- DOLPHIN: Learning clause-selection rules for dynamic optimization of logic programs