7 research outputs found
On the Complexity of Case-Based Planning
We analyze the computational complexity of problems related to case-based
planning: planning when a plan for a similar instance is known, and planning
from a library of plans. We prove that planning from a single case has the same
complexity than generative planning (i.e., planning "from scratch"); using an
extended definition of cases, complexity is reduced if the domain stored in the
case is similar to the one to search plans for. Planning from a library of
cases is shown to have the same complexity. In both cases, the complexity of
planning remains, in the worst case, PSPACE-complete
Parameterized Complexity Results for Plan Reuse
Planning is a notoriously difficult computational problem of high worst-case
complexity. Researchers have been investing significant efforts to develop
heuristics or restrictions to make planning practically feasible. Case-based
planning is a heuristic approach where one tries to reuse previous experience
when solving similar problems in order to avoid some of the planning effort.
Plan reuse may offer an interesting alternative to plan generation in some
settings.
We provide theoretical results that identify situations in which plan reuse
is provably tractable. We perform our analysis in the framework of
parameterized complexity, which supports a rigorous worst-case complexity
analysis that takes structural properties of the input into account in terms of
parameters. A central notion of parameterized complexity is fixed-parameter
tractability which extends the classical notion of polynomial-time tractability
by utilizing the effect of structural properties of the problem input.
We draw a detailed map of the parameterized complexity landscape of several
variants of problems that arise in the context of case-based planning. In
particular, we consider the problem of reusing an existing plan, imposing
various restrictions in terms of parameters, such as the number of steps that
can be added to the existing plan to turn it into a solution of the planning
instance at hand.Comment: Proceedings of AAAI 2013, pp. 224-231, AAAI Press, 201
On the use of case-based planning for e-learning personalization
This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques
to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical
and students’ requirements.
myPTutor has a potential applicability to support e-learning personalization by producing, and automatically
solving, a planning model from (and to) e-learning standards in a vast number of real scenarios,
from small to medium/large e-learning communities. Our experiments demonstrate that we can solve
scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools,
high schools and universities, especially if they already use Moodle, on top of which we have implemented
myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the
students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces
the differences between the original and the new route, thus enhancing the learning process.
© 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156
Formal verification: further complexity issues and applications
Prof. Giacomo Cioffi (Università di Roma "La Sapienza"), Prof. Fabio Panzieri (Università di Bologna), Dott.ssa Carla Limongelli (Università di Roma Tre)
Using case-based planning to assist local-search-based planning
Master'sMASTER OF ENGINEERIN