Towards an automatic monitoring for higher education learning design

The development of new Information Technologies (IT) has originated new possibilities to design pedagogical methodologies that provide the necessary knowledge and skills in the higher education. This paper presents a metadata-based model representation that is used to represent, detect, and even automatically correct possible pitfalls in the schedule process of a Learning Design (LD) in e-learning environments. This metadata-based model is combined with Artificial Intelligence techniques, such as, planning and scheduling to monitor how is evolving a particular LD, and to propose solutions in those modules of the design that learning problems among the students have been found.This work was funded by the Universidad de Alcalá project UAH PI2005/084 and the CICYT project TSI2006- 12085

Confidence intervals of success rates in evolutionary computation

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 12th annual conference on Genetic and evolutionary computation , http://dx.doi.org/10.1145/1830483.1830657Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studie

Adapting Searchy to extract data using evolved wrappers

This is the author’s version of a work that was accepted for publication inExpert Systems with Applications: An International Journal. 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: An International Journal, 39, 3 (2012) DOI: 10.1016/j.eswa.2011.08.168Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples.The authors gratefully acknowledge Mart´ın Knoblauch for his useful suggestions and valuable comments. This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects ABANT (TIN 2010-19872), COMPUBIODIVE (TIN2007-65989) and by Castilla-La Mancha project PEII09-0266-6640

AI techniques for automatic learning design

This is an electronic version of the paper presented at the International e-Conference of Computer Science 2006, held online on 2006This paper presents a new approach to the problem of control and monitoring Learning Design courses. Our approach uses the integration of AI planning and scheduling as the main solving/reasoning process. These techniques are used to solve some problems in a particular Learning Design. Those problems will be detected from the educators/students interactions, and it will be necessary to map both, the metadata provided by the educators, and the results obtained from previous interactions, into an appropriate representation that could be used by any planner (and/or scheduler) to reason about plans. This paper describes how AI planning and scheduling techniques can help in detect and solve problems in the Learning Design

A decision support system for logistics operations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-13161-5_14Proceedings of 5th International Workshop Soft Computing Models in Industrial and Environmental ApplicationsThis paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.We want to thank Antonio Montoya for his contribution in the tool developed. This work has been supported by the Espi & Le Barbier company and the public projects funded by the Spanish Ministry of Science and Innovation under the projects COMPUBIODIVE (TIN2007-65989), V-LeaF (TIN2008-02729-E/TIN) and by Castilla-La Mancha project PEII09- 0266-6640

Effects of the lack of selective pressure on the expected run-time distribution in genetic programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castano, and D. Camacho, "Effects of the lack of selective pressure on the expected run-time distribution in genetic programming", in IEEE Congress on Evolutionary Computation, CEC 2013, pp. 1748 - 1755Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.This work has been partly supported by Spanish Ministry of Science and Education under project ABANT (TIN2010- 19872)

An empirical study on the accuracy of computational effort in Genetic Programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.This work was partially supported by the MICYT project ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-664

Integrating planning and scheduling in workflow domains

One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as business process reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models. As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates planning and scheduling, IPSS. We present results in a real workflow domain, the telephone installation (TI) domain.The SHAMASH project has being carried out in the course of the R&D project funded by the Esprit Program of the Commission of the European Communities as project number 25491. A complementary grant was given by the Spanish research commission, CICYT, under project number TIC98-1847-CE. We thank the partners of this project, who have originated and contributed to the ideas reported: UF (Unio´n Fenosa), SAGE (Software AG Espan˜ a), SEMA GROUP sae, UC3M (Universidad Carlos III de Madrid), WIP (Wirstchaft und infrastruktur & Co Planungs KG), and EDP (Electricidade de Portugal). We would specially like to thank all the UC3M team, the PLANET people and Paul Kearney (BT). Through talks with him we have outlined many ideas. This work has also been partially funded by grant MCyT TIC2002-04146-C05-05 and the UAH project PI2005/084.Publicad

Acquisition of business intelligence from human experience in route planning

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 2015, available online at:http://www.tandfonline.com/10.1080/17517575.2012.759279The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e., plan the routes that the shippers have to follow to deliver the goods. In this paper we present an AI-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimized routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimizes the delivery process. The solution uses Data Mining to extract knowledge from the company information systems and prepares it for analysis with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a Genetic Algorithm (GA) that, given the processed information, optimizes the routes following several objectives, such as minimize the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, in average, the routes made by the human experts.This work has been partially supported by the SpanishMinistry of Science and Innovation under the projects ABANT (TIN 2010-19872) and by Jobssy.com company under Project FUAM-076913

Distributed parameter tuning for genetic algorithms

Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite the excellent results of GAs, their use may generate new problems. One of them is how to provide a good fitting in the usually large number of parameters that must be tuned to allow a good performance. This paper describes a new platform that is able to extract the Regular Expression that matches a set of examples, using a supervised learning and agent-based framework. In order to do that, GA-based agents decompose the GA execution in a distributed sequence of operations performed by them. The platform has been applied to Language induction problem, for that reason the experiments are focused on the extraction of the regular expression that matches a set of examples. Finally, the paper shows the efficiency of the proposed platform (in terms of fitness value) applied to three case studies: emails, phone numbers and URLs. Moreover, it is described how the codification of the alphabet affects to the performance of the platform.This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects COMPUBIODIVE(TIN2007-65989), V-LeaF (TIN2008-02729-E/TIN), Castilla-La Mancha project PEII09-0266-6640 and HADA (TIN2007-64718)