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Hand-encoding PDDL domains is generally accepted as difficult, tedious anderror-prone. The difficulty is even greater when temporal domains have to beencoded. Indeed, actions have a duration and their effects are notinstantaneous. In this paper, we present TempAMLSI, an algorithm based on theAMLSI approach able to learn temporal domains. TempAMLSI is based on theclassical assumption done in temporal planning that it is possible to convert anon-temporal domain into a temporal domain. TempAMLSI is the first approachable to learn temporal domain with single hard envelope and Cushing'sintervals. We show experimentally that TempAMLSI is able to learn accuratetemporal domains, i.e., temporal domain that can be used directly to solve newplanning problem, with different forms of action concurrency.

TempAMLSI : Temporal Action Model Learning based on Grammar Induction