Learning Applicability Conditions in AI Planning from Partial Observations

AI planning has become more and more important in many real-world domains such as military applications and intelligent scheduling. However, planning systems require complete specifications of domain models, which can be difficult to encode, even for domain experts. Thus, research on effective and efficient methods to construct domain models or applicability conditions for planning automatically has become a hot topic for researchers. In this paper, we review our previous work ARMS, which can learn the applicability conditions for planning under STRIPS representations. Moreover, we provide two extensions to our ARMS system, LAMP, which can learn complex action models in PDDL representations with quantifiers and logical implications, and HTN-Learner, which can simultaneously learn method preconditions and action models in hierarchical task network (HTN) models. Our experimental results show that the two proposed algorithms could effectively learn complex action models and HTN models, thus having the ability to effectively acquire applicability conditions and relationships between actions in AI planning.