Nowadays have already passed more than two years since PATRICIA research project about using pet robots to reduce pain and anxiety in hospitalized children was started and the advances made are more than significant. Patients, parents, nurses, psychologists, engineers... all of them have adopted with illusion Pleo robot, a kind of robotic pet, working hard looking for new procedures and new solutions to the current pediatrics diseases. From this work, a technological contribution is provided going one step beyond to what seems a long path. Concretely, it is wanted to develop a system to wirelessly communicate with Pleo in order to help the coordinator who leads the therapy with the kid, to understand and control Pleo’s behaviour at any moment. This article explains how this technological part is being developed and obtained technical results.
In the present work, the Fuzzy Inductive Reasoning methodology (FIR) is used to improve coherence among beat patterns, structured in a musical A-B form. Patterns were generated based on a probability matrix, encoding a particular musical style, designed by experts. Then, all possible patterns were generated and the most probables were selected. A-B musical forms were created and the coherence of the sequence was evaluated by experts by using linguistic quantities. The output pairs (A-B pattern and its qualification) were used as inputs to train a FIR system, and the variables that produce “coherent” outputs and the relations among
them where identified as rules. The extracted rules are discussed in the context of the musical form and from the psychological perception.
In the present work, the Fuzzy Inductive Reasoning methodology (FIR) is used to improve coherence among beat patterns, structured in a musical A-B form. Patterns were generated based on a probability matrix, encoding a particular musical style, designed by experts. Then, all possible patterns were generated and the most probables were selected. A-B musical forms were created and the coherence of the sequence was evaluated by experts by using linguistic quantities. The output pairs (A-B pattern and its qualification) were used as inputs to train a FIR system, and the variables that produce “coherent” outputs and the relations among them where identified as rules. The extracted rules are discussed in the context of the musical form and from the psychological perception.
In this paper, a new formulation of Creativity is presented in the context of Creativity Support Systems. This formulation is based on the central ideas of the theory of Boden. It redefines some concepts such as appropriateness and relevance in order to allow the implementation of a support system for creative people. The approach is based on the conceptual space proposed by Boden and formalized by other authors. The presented
formulation is applied to a real case in which a new chocolate cake with fruit is design. Data collected from a Spanish chocolate chef has been used to validate the proposed system. Experimental results show that the formulation presented is not only useful for understanding how the creative mechanisms of design works, but also
facilitates its implementation in real cases to support creativity processes.
Navarro, J.; Sancho-Asensio, A.; Garriga, C.; Albo-Canals, J.; Ortiz-Villajos Maroto, J.; Raya, C.; Angulo, C.; Miralles, D. IROS Workshop on Cloud Robotics Presentation's date: 2013-11-03 Presentation of work at congresses
Robots and automation systems have become a
valuable partner in several facets of human life: from learning
and teaching, to daily working, including health monitoring
and assistance. So far, these appealing robot-based applications
are restricted to conduct repetitive, yet useful, tasks due to the
reduced individual robots’ capabilities in terms of processing
and computation. This concern prevents current robots from
facing more complex applications related to understanding hu-
man beings and perceiving their subtle feelings. Such hardware
limitations have been already found in the computer science
field. In this domain, they are currently being addressed using
a new resource exploitation model coined as cloud computing,
which is targeted at enabling massive storage and computation
using smartly connected and inexpensive commodity hardware.
The purpose of this paper is to propose a cloud-based robotics
architecture to effectively develop complex tasks related to
hospitalized children assistance. More specifically, this paper
presents a multi-agent learning system that combines machine
learning and cloud computing using low-cost robots to (1)
collect and perceive children status, (2) build a human-readable
set of rules related to the child-robot relationship, and (3)
improve the children experience during their stay in the hos-
pital. Conducted preliminary experiments proof the feasibility
of this proposal and encourage practitioners to work towards
It is well known that some people can perform a
task with greater precision and accuracy than
others: they are experts. In the past, experts were
interviewed to find out why they have this expertise,
but this was not always completely effective
because often experts "don't know what they
know". In this paper we propose a model of the
process of making decisions performed by experts
in the final adjustment of products task. Based on
this model, we also propose a system based on a
machine learning module that facilitates the capture
of these expert skills. We give an example to illustrate
the process proposed.
Integrated Vehicle Health Management (IVHM) systems on modern aircraft or autonomous unmanned vehicles should provide diagnostic and prognostic capabilities with lower support costs and amount of data traffic. When mission objectives cannot be reached for the control system since unanticipated operating conditions exists, namely a failure, the mission plan must be revised or altered according to the health monitoring system assessment. Representation of the system health knowledge must facilitate interaction with the control system to compensate for subsystem degradation. Several generic architectures have been described for the implementation of health monitoring systems and their integration with the control system. In particular, the Open System Architecture - Condition-Based Maintenance (OSA-CBM) approach is considered in this work as initial point, and it is evolved in the sense of self-health awareness, by defining an appropriated multi-agent smart health management architecture based on smart device models, communication agents and a distributed control system. A case study about its application on fuel-cells as auxiliary power generator will demonstrate the integration.