2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations
M. Grand, H. Fiorino, D. Pellier
The Hierarchical Task Network ({\sf HTN}) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical {\sf STRIPS} formalism…
HDDL 2.1: Towards Defining an HTN Formalism with Time
D. Pellier, H. Fiorino, M. Grand +2
Real world applications of planning, like in industry and robotics, require modelling rich and diverse scenarios. Their resolution usually requires coordinated and concurrent actio…
TempAMLSI : Temporal Action Model Learning based on Grammar Induction
Maxence Grand, Damien Pellier, Humbert Fiorino
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 hav…
AMLSI: A Novel Accurate Action Model Learning Algorithm
Maxence Grand, Humbert Fiorino, Damien Pellier
This paper presents new approach based on grammar induction called AMLSI Action Model Learning with State machine Interactions. The AMLSI approach does not require a training datas…