TempAMLSI: Temporal Action Model Learning Based on STRIPS Translation
Keywords:Action Model Learning, Temporal Domain Learning, STRIPS Translation
AbstractHand-encoding PDDL domains is generally considered difficult, tedious and error-prone. The difficulty is even greater when temporal domains have to be encoded. Indeed, actions have a duration and their effects are not instantaneous. In this paper, we present TempAMLSI, an algorithm based on the AMLSI approach to learn temporal domains. TempAMLSI is the first approach able to learn temporal domains with single hard envelopes, and TempAMLSI is the first approach able to deal with both partial and noisy observations. We show experimentally that TempAMLSI learns accurate temporal domains, i.e., temporal domains that can be used without human proofreading to solve new planning problems with different forms of action concurrency.
How to Cite
Grand, M., Pellier, D., & Fiorino, H. (2022). TempAMLSI: Temporal Action Model Learning Based on STRIPS Translation. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 597-605. https://doi.org/10.1609/icaps.v32i1.19847
Planning and Learning Track