Learning and Tuning Meta-heuristics in Plan Space Planning
  In recent years, the planning community has observed that techniques for
learning heuristic functions have yielded improvements in performance. One
approach is to use offline learning to learn predictive models from existing
heuristics in a domain dependent manner. These learned models are deployed as
new heuristic functions. The learned models can in turn be tuned online using a
domain independent error correction approach to further enhance their
informativeness. The online tuning approach is domain independent but instance
specific, and contributes to improved performance for individual instances as
planning proceeds. Consequently it is more effective in larger problems.
  In this paper, we mention two approaches applicable in Partial Order Causal
Link (POCL) Planning that is also known as Plan Space Planning. First, we
endeavor to enhance the performance of a POCL planner by giving an algorithm
for supervised learning. Second, we then discuss an online error minimization
approach in POCL framework to minimize the step-error associated with the
offline learned models thus enhancing their informativeness. Our evaluation
shows that the learning approaches scale up the performance of the planner over
standard benchmarks, specially for larger problems.
