Evaluating Planning Model Learning Algorithms

Abstract

Formulating domain models for model-based planning is a challenging, time consuming, and error-prone task. A number of approaches have been proposed to automatically learn domain models from a given set of observations. A key question is how to compare models learned by different approaches. Currently, there are no standard evaluation metrics or benchmarks. In this paper, we describe a set of metrics designed to assess different characteristics of a learned domain model. We then present a benchmark suite based on domain models from the International Planning Competition (IPC) and an evaluation process for using it. Four domain model learning algorithms are evaluated on this benchmark, which highlights the importance of the diverse evaluation metrics we proposed. Show more