E-Learning and Intelligent Planning: Improving Content Personalization

Combining learning objects is a challenging topic because of its direct application to curriculum generation, tailored to the students' profiles and preferences. Intelligent planning allows us to adapt learning routes (i.e. sequences of learning objects), thus highly improving the personalization of contents, the pedagogical requirements and specific necessities of each student. This paper presents a general and effective approach to extract metadata information from the e-learning contents, a form of reusable learning objects, to generate a planning domain in a simple, automated way. Such a domain is used by an intelligent planner that provides an integrated recommendation system, which adapts, stores and reuses the best learning routes according to the students' profiles and course objectives. If any inconsistency happens during the route execution, e.g. the student fails to pass an assessment test which prevents him/her from continuing the natural course of the route, the systeGarrido, A.; Morales, L. (2014). E-Learning and Intelligent Planning: Improving Content Personalization. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje. 9(1):1-7. doi:10.1109/RITA.2014.2301886S179

Agentes inteligentes en ambientes dinámicos

El objetivo general de este Proyecto de Investigación es el estudio y desarrollo de técnicas de Inteligencia Artificial para dotar de inteligencia y conocimiento a agentes inmersos en mundos virtuales, interactivos y dinámicos. El énfasis es puesto tanto en formalismos de planificación y razonamiento rebatible para la creación y control de agentes inteligentes, como en el impacto que tienen las tecnologías del lenguaje humano (TLH) en la inclusión social. En estos escenarios, el razonamiento, la toma de decisiones, la planificación de acciones y el aprendizaje ocurren bajo restricciones de tiempo críticas y en intensa interacción con el usuario.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Uma nova abordagem de aprendizagem de máquina combinando elicitação automática de casos, aprendizagem por reforço e mineração de padrões sequenciais para agentes jogadores de damas

ake into account, in addition to the environment, the minimizing action of an opponent (such as in games), it is fundamental that the agent has the ability to progressively trace a proĄle of its adversary that aids it in the process of selecting appropriate actions. However, it would be unsuitable to construct an agent with a decision-making system based on only the elaboration of this proĄle, as this would prevent the agent from having its Şown identityŤ, which would leave it at the mercy of its opponent. Following this direction, this work proposes an automatic hybrid Checkers player, called ACE-RL-Checkers, equipped with a dynamic decision-making mechanism, which adapts to the proĄle of its opponent over the course of the game. In such a system, the action selection process (moves) is conducted through a composition of Multi-Layer Perceptron Neural Network and case library. In the case, Neural Network represents the ŞidentityŤ of the agent, i.e., it is an already trained static decision-making module and makes use of the Reinforcement Learning TD( ) techniques. On the other hand, the case library represents the dynamic decision-making module of the agent, which is generated by the Automatic Case Elicitation technique (a particular type of Case-Based Reasoning). This technique has a pseudo-random exploratory behavior, which makes the dynamic decision-making on the part of the agent to be directed, either by the game proĄle of the opponent or randomly. However, when devising such an architecture, it is necessary to avoid the following problem: due to the inherent characteristics of the Automatic Case Elicitation technique, in the game initial phases, in which the quantity of available cases in the library is extremely low due to low knowledge content concerning the proĄle of the adversary, the decisionmaking frequency for random decisions is extremely high, which would be detrimental to the performance of the agent. In order to attack this problem, this work also proposes to incorporate onto the ACE-RL-Checkers architecture a third module composed of a base of experience rules, extracted from games played by human experts, using a Sequential Pattern Mining technique. The objective behind using such a base is to reĄne and accelerate the adaptation of the agent to the proĄle of its opponent in the initial phases of their confrontations. Experimental results conducted in tournaments involving ACE-RL-Checkers and other agents correlated with this work, conĄrm the superiority of the dynamic architecture proposed herein.Fundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)Agentes que operam em ambientes onde as tomadas de decisão precisam levar em conta, além do ambiente, a atuação minimizadora de um oponente (tal como nos jogos), é fundamental que o agente seja dotado da habilidade de, progressivamente, traçar um perĄl de seu adversário que o auxilie em seu processo de seleção de ações apropriadas. Entretanto, seria improdutivo construir um agente com um sistema de tomada de decisão baseado apenas na elaboração desse perĄl, pois isso impediria o agente de ter uma Şidentidade própriaŤ, o que o deixaria a mercê de seu adversário. Nesta direção, este trabalho propõe um sistema automático jogador de Damas híbrido, chamado ACE-RL-Checkers, dotado de um mecanismo dinâmico de tomada de decisões que se adapta ao perĄl de seu oponente no decorrer de um jogo. Em tal sistema, o processo de seleção de ações (movimentos) é conduzido por uma composição de Rede Neural de Perceptron Multicamadas e biblioteca de casos. No caso, a Rede Neural representa a ŞidentidadeŤ do agente, ou seja, é um módulo tomador de decisões estático já treinado e que faz uso da técnica de Aprendizagem por Reforço TD( ). Por outro lado, a biblioteca de casos representa o módulo tomador de decisões dinâmico do agente que é gerada pela técnica de Elicitação Automática de Casos (um tipo particular de Raciocínio Baseado em Casos). Essa técnica possui um comportamento exploratório pseudo-aleatório que faz com que a tomada de decisão dinâmica do agente seja guiada, ora pelo perĄl de jogo do adversário, ora aleatoriamente. Contudo, ao conceber tal arquitetura, é necessário evitar o seguinte problema: devido às características inerentes à técnica de Elicitação Automática de Casos, nas fases iniciais do jogo Ű em que a quantidade de casos disponíveis na biblioteca é extremamente baixa em função do exíguo conhecimento do perĄl do adversário Ű a frequência de tomadas de decisão aleatórias seria muito elevada, o que comprometeria o desempenho do agente. Para atacar tal problema, este trabalho também propõe incorporar à arquitetura do ACE-RLCheckers um terceiro módulo, composto por uma base de regras de experiência extraída a partir de jogos de especialistas humanos, utilizando uma técnica de Mineração de Padrões Sequenciais. O objetivo de utilizar tal base é reĄnar e acelerar a adaptação do agente ao perĄl de seu adversário nas fases iniciais dos confrontos entre eles. Resultados experimentais conduzidos em torneio envolvendo ACE-RL-Checkers e outros agentes correlacionados com este trabalho, conĄrmam a superioridade da arquitetura dinâmica aqui proposta

An Educational Framework to Support Industrial Control System Security Engineering

Industrial Control Systems (ICSs) are used to monitor and control critical infrastructure such as electricity and water. ICS were originally stand-alone systems, but are now widely being connected to corporate national IT networks, making remote monitoring and more timely control possible. While this connectivity has brought multiple benefits to ICS, such as cost reductions and an increase in redundancy and flexibility, ICS were not designed for open connectivity and therefore are more prone to security threats, creating a greater requirement for adequate security engineering approaches. The culture gap between developers and security experts is one of the main challenges of ICS security engineering. Control system developers play an important role in building secure systems; however, they lack security training and support throughout the development process. Security training, which is an essential activity in the defence-indepth strategy for ICS security, has been addressed, but has not been given sufficient attention in academia. Security support is a key means by which to tackle this challenge via assisting developers in ICS security by design. This thesis proposes a novel framework, the Industrial Control System Security Engineering Support (ICS-SES), which aims to help developers in designing secure control systems by enabling them to reuse secure design patterns and improve their security knowledge. ICS-SES adapts pattern-based approach to guide developers in security engineering, and an automated planning technique to provide adaptive on-the-job security training tailored to personal needs. The usability of ICS-SES has been evaluated using an empirical study in terms of its effectiveness in assisting the design of secure control systems and improving developers’ security knowledge. The results show that ICS-SES can efficiently help control system designers to mitigate security vulnerabilities and improve their security knowledge, reducing the difficulties associated with the security engineering process, and the results have been found to be statically significant. In summary, ICS-SES provides a unified method of supporting an ICS security by design approach. It fosters a development environment where engineers can improve their security knowledge while working in a control system production line.Libyan Embassy in London, U

Using AI planning to enhance e-learning processes

3siThis work describes an approach that automatically extracts standard metadata information from e-learning contents, combines it with the student preferences/goals and creates PDDL planning domains+problems. These PDDL problems can be solved by current planners, although we motivate the use and benefits of case-based planning techniques, to obtain fully tailored learning routes that significantly enhance the learning process. During the execution of a given route, a monitoring phase is used to detect discrepancies, i.e. flaws that prevent the student from continuing with the original plan. In such a situation, an adaptation mechanism becomes necessary to fix the flaws, while also trying to minimise the differences between the original and the new route. We have integrated this approach on top of Moodle and experimented with 100 benchmark problems to evaluate the quality, scalability and viability of the system. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.nonenoneA. Garrido; L. Morales; I. Serina.A., Garrido; L., Morales; Serina, Iva