Learning and Tuning Meta-heuristics in Plan Space Planning

TL;DR

This paper explores learning heuristic functions and online tuning in plan space planning, demonstrating improved performance on benchmarks, especially for larger problems, through supervised learning and error minimization techniques.

Contribution

It introduces two novel approaches for POCL planning: supervised learning of heuristics and online error correction to enhance planner efficiency.

Findings

Learning approaches improve planner performance on benchmarks.

Online tuning benefits larger problem instances.

Methods scale effectively with problem size.

Abstract

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.