Learning to Rank for Synthesizing Planning Heuristics
This work addresses the problem of improving planning efficiency for AI planners, but it is incremental as it builds on prior regression-based methods with a ranking approach.
The authors tackled the problem of learning domain-specific planning heuristics by framing it as a learning-to-rank task instead of regression, using RankSVM, and introduced new features for temporal interactions. Their experiments on International Planning Competition problems showed that the RankSVM learned heuristics outperformed both original heuristics and regression-based learned heuristics.
We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner's performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation. Additionally, we introduce new methods for computing features that capture temporal interactions in an approximate plan. Our experiments on recent International Planning Competition problems show that the RankSVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression.