TempAMLSI : Temporal Action Model Learning based on Grammar Induction

TL;DR

TempAMLSI is a novel algorithm that automatically learns temporal planning domains with accurate action durations and effects, simplifying the creation of temporal domain models for planning tasks.

Contribution

It introduces the first approach capable of learning temporal domains with a single hard envelope and Cushing's intervals, based on grammar induction from non-temporal domains.

Findings

TempAMLSI can learn accurate temporal domains for planning.

The learned domains support different forms of action concurrency.

Experimental results show effectiveness in real planning scenarios.

Abstract

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.