@Article{CruzSilvFerrBern:2021:AuPlIn,
author = "Cruz, Caio Gustavo Rodrigues da and Silva, Rodrigo Rocha and
Ferreira, Maur{\'{\i}}cio Gon{\c{c}}alves Vieira and
Bernardino, Jorge",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Coimbra} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Polytechnic of Coimbra}",
title = "Automated Planning With Invalid States Prediction",
journal = "IEEE Access",
year = "2021",
volume = "9",
pages = "68289--68301",
keywords = "Planning, Satellites, Machine learning, Data mining, Transforms,
Process control, Computer architecture, Automated planning, domain
rule learning, machine learning, PDDL.",
abstract = "The increase of automated systems in space missions raises
concerns about safety and reliability in operations carried out by
satellites due to performance degradation. There have been several
studies about the automatic planning process, but many approaches
are generated with invalid states. The invalid state can be
understood as a prohibited, degraded or risky scenario for the
domain. This paper proposes an automated planning process with
restrictions that enables automatic planners to not generate plans
with invalid states. We implement a validator method for the
planner software which proves that plan generation matches the
restrictions imposed on the domain. In the experiments, we test an
automatic planning process that is specific to the aerospace area,
where a knowledge base with invalid states is available in the
context of the operation of a satellite. Our proposal to carry out
the verification of invalid states in automatic planning, can
contribute to plans being generated with higher quality, ensuring
that the goal of a plan is only achieved through valid
intermediate states. It is also expected that the generated plans
will be executed with better performance and will require less
computational resources, since the search space is reduced.",
doi = "10.1109/ACCESS.2021.3077521",
url = "http://dx.doi.org/10.1109/ACCESS.2021.3077521",
issn = "2169-3536",
language = "en",
targetfile = "cruz_automated.pdf",
urlaccessdate = "03 jun. 2024"
}