Macro learning in planning as parameter configuration

• Authors: Maher Alhossaini;J. Christopher Beck
• Venue: Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
• Year: 2012

In AI planning, macro learning is the task of finding sub-sequences of operators that can be added to the planning domain to improve planner performance. Typically, a single set is added to the domain for all problem instances. A number of techniques have been developed to generate such a macro set based on offline analysis of problem instances. We build on recent work on instance-specific and fixed-set macros, and recast the macro generation problem as parameter configuration: the macros in a domain are viewed as parameters of the planning problem. We then apply an existing parameter configuration system to reconfigure a domain either once or per problem instance. Our empirical results demonstrate that our approach outperforms existing macro acquisition and filtering tools. For instance-specific macros, our approach almost always achieves equal or better performance than a complete evaluation approach, while often being an order of magnitude faster offline.