URBANO: A Tour-Guide Robot Learning to Make Better Speeches

—Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profil

Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction

Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches

Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described