TempAMLSI : Temporal Action Model Learning based on Grammar Induction
This addresses the problem of automating temporal domain encoding for planning systems, offering a novel solution but likely incremental as it builds on the AMLSI approach.
The paper tackles the challenge of hand-encoding temporal PDDL domains, which is difficult and error-prone, by presenting TempAMLSI, an algorithm that learns accurate temporal domains from data, enabling direct use in solving new planning problems with various forms of action concurrency.
Hand-encoding PDDL domains is generally accepted as 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 able to learn temporal domains. TempAMLSI is based on the classical assumption done in temporal planning that it is possible to convert a non-temporal domain into a temporal domain. TempAMLSI is the first approach able to learn temporal domain with single hard envelope and Cushing's intervals. We show experimentally that TempAMLSI is able to learn accurate temporal domains, i.e., temporal domain that can be used directly to solve new planning problem, with different forms of action concurrency.