Transfer Learning Action Models by Measuring the Similarity of Different Domains

Abstract

AI planning requires action models to be given in advance. However, it is both time consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL, as a result, learning action models is important for AI planning. On the other hand, the data being used to learn action models are often limited in planning domains, which makes the learning task very difficult. In this paper, we present a new algorithm to learn action models from plan traces by transferring useful information from other domains whose action models are already known. We present a method of building a metric to measure the shared information and transfer this information according to this metric. The larger the metric is, the bigger the information is transferred. In the experiment result, we show that our proposed algorithm is effective.