84 research outputs found
Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning
Case-Based planning can fruitfully exploit knowledge
gained by solving a large number of problems, storing
the corresponding solutions in a plan library and reusing
them for solving similar planning problems in the future.
Case-based planning is extremely effective when
similar reuse candidates can be efficiently chosen.
In this paper, we study an innovative technique based
on planning problem features for efficiently retrieving
solved planning problems (and relative plans) from
large plan libraries. A problem feature is a characteristic
of the instance that can be automatically derived from
the problem specification, domain and search space
analyses, and different problem encodings.
Since the use of existing planning features are not always
able to effectively distinguish between problems
within the same planning domain, we introduce a new
class of features.
An experimental analysis in this paper shows that our
features-based retrieval approach can significantly improve
the performance of a state-of-the-art case-based
planning system
On the Necessity of Time and Resource Issues to Support Collaboration in E-learning Standards
In this paper we motivate the necessity of time+resource metadata in current e-learning
standards to support collaborative activities. Learning Objects (LOs) are currently defined in
a very independent way from each other, which makes it difficult to use them in a real
scenario where students interact and have their own constraints. We present some challenging
features that, at least, should be discussed when elaborating new e-learning standards.Garrido Tejero, A.; Morales, L.; Serina, I. (2011). On the Necessity of Time and Resource Issues to Support Collaboration in E-learning Standards. IEEE Learning Technology Newsletter. 13:39-41. http://hdl.handle.net/10251/35041S39411
Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy
Abstract Accurate streamflow prediction is a fundamental task for integrated water resources management and flood risk mitigation. The purpose of this study is to forecast the water inflow to lake Como, (Italy) using different machine learning algorithms. The forecast is done for different days ranging from one day to three days. These models are evaluated by three statistical measures including Mean Absolute Error, Root Mean Squared Error, and the Nash-Sutcliffe Efficiency Coefficient. The experimental results show that Neural Network performs better for streamflow estimation with MAE and RMSE followed by Support Vector Regression and Random Forest
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
Generazione ed adattamento di piani attraverso grafi di pianificazione: sviluppo e sperimentazione di algoritmi basati su ricerca locale e backtracking
Dottorato di ricerca in ingegneria dell'informazione. 12. ciclo. Supervisore A. GereviniConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal
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