Exploring the Synergy between two Modular Learning Techniques for Automated Planning

In the last decade the emphasis on improving the operational
performance of domain independent automated planners
has been in developing complex techniques which merge
a range of different strategies. This quest for operational advantage,
driven by the regular international planning competitions,
has not made it easy to study, understand and predict
what combinations of techniques will have what effect
on a planner’s behaviour in a particular application domain.
In this paper, we consider two machine learning techniques
for planner performance improvement, and exploit a modular
approach to their combination in order to facilitate the analysis
of the impact of each individual component. We believe
this can contribute to the development of more transparent
planning engines, which are designed using modular, interchangeable,
and well-founded components. Specifically, we
combined two previously unrelated learning techniques, entanglements
and relational decision trees, to guide a “vanilla”
search algorithm. We report on a large experimental analysis
which demonstrates the effectiveness of the approach in terms
of performance improvements, resulting in a very competitive
planning configuration despite the use of a more modular and
transparent architecture. This gives insights on the strengths
and weaknesses of the considered approaches, that will help
their future exploitation.