5,020 research outputs found
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Grammar-based Representation and Identification of Dynamical Systems
In this paper we propose a novel approach to identify dynamical systems. The
method estimates the model structure and the parameters of the model
simultaneously, automating the critical decisions involved in identification
such as model structure and complexity selection. In order to solve the
combined model structure and model parameter estimation problem, a new
representation of dynamical systems is proposed. The proposed representation is
based on Tree Adjoining Grammar, a formalism that was developed from linguistic
considerations. Using the proposed representation, the identification problem
can be interpreted as a multi-objective optimization problem and we propose a
Evolutionary Algorithm-based approach to solve the problem. A benchmark example
is used to demonstrate the proposed approach. The results were found to be
comparable to that obtained by state-of-the-art non-linear system
identification methods, without making use of knowledge of the system
description.Comment: Submitted to European Control Conference (ECC) 201
All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch
Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts
and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten
different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information
Inference in classifier systems
Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed
Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies
The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin
Automatically correcting syntactic and semantic errors in ATL transformations using multi-objective optimization
L’ingénierie dirigée par les modèles (EDM) est un paradigme de développement logiciel
qui promeut l’utilisation de modèles en tant qu’artefacts de première plan et de processus
automatisés pour en dériver d’autres artefacts tels que le code, la documentation et les cas de
test. La transformation de modèle est un élément important de l’EDM puisqu’elle permet de
manipuler les représentations abstraites que sont les modèles. Les transformations de modèles,
comme d’autres programmes, sont sujettes à la fois à des erreurs syntaxiques et sémantiques.
La correction de ces erreurs est difficile et chronophage, car les transformations dépendent
du langage de transformation comme ATL et des langages de modélisation dans lesquels
sont exprimés les modèles en entrée et en sortie. Les travaux existants sur la réparation
des transformations ciblent les erreurs syntaxiques ou sémantiques, une erreur à la fois, et
définissent manuellement des patrons de correctifs. L’objectif principal de notre recherche
est de proposer un cadre générique pour corriger automatiquement de multiples erreurs
syntaxiques et sémantiques. Afin d’atteindre cet objectif, nous reformulons la réparation des
transformations de modèles comme un problème d’optimisation multiobjectif et le résolvons au
moyen d’algorithmes évolutionnaires. Pour adapter le cadre aux deux catégories d’erreurs, nous
utilisons différents types d’objectifs et des stratégies sophistiquées pour guider l’exploration
de l’espace des solutions.Model-driven engineering (MDE) is a software development paradigm that promotes the
use of models as first-class artifacts and automated processes to derive other artefacts from
them such as code, documentation and test cases. Model transformation is an important
element of MDE since it allows to manipulate the abstract representations that are models.
Model transformations, as other programs are subjects to both syntactic and semantic errors.
Fixing those errors is difficult and time consuming as the transformations depend on the
transformation language such as ATL, and modeling languages in which input and output
models are expressed. Existing work on transformation repair targets either syntactic or
semantic errors, one error at a time, and define patch templates manually. The main goal of
our research is to propose a generic framework to fix multiple syntactic and semantic errors
automatically. In order to achieve this goal, we reformulate the repair of model transformations
as a multi-objective optimization problem and solve it by means of evolutionary algorithms.
To adapt the framework to the two categories of errors, we use different types of objectives
and sophisticated strategies to guide the search
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