TempAMLSI : Temporal Action Model Learning based on Grammar Induction

Maxence Grand (1) , Damien Pellier (1) , Humbert Fiorino (1)

1 Marvin - Artificial Intelligence and Robotics

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. Tem-pAMLSI 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.

Domaines: Intelligence artificielle [cs.AI]

Maxence Grand, Damien Pellier, Humbert Fiorino. TempAMLSI : Temporal Action Model Learning based on Grammar Induction. International workshop of Knowledge Engineering for Planning and Scheduling (ICAPS), Aug 2021, Guangzhou, China. ⟨hal-03374373⟩