Traffic Sign Recognition System

The research group CAOS at the Computing Department of the Carlos III University of Madrid, Spain, offers an efficient recognition system for traffic signs using a set of classifiers. This system can be used as part of an active security system in cars. The fact that the system is based on a set of classifiers facilitates a distributed implementation, resulting in cheaper hardware and an improvement in fault-tolerance.Contrato Programa de Comercialización e Internacionalización. Sistema Regional de Investigación Científica e Innovación Tecnológica. (Comunidad de Madrid; Universidad Carlos III de Madrid

Sistema de Identificación de Señales de Tráfico

El grupo CAOS del Departamento de Informática de la Universidad Carlos III de Madrid (España) ofrece un sistema para la identificación eficiente de señales de tráfico mediante conjuntos de clasificadores. Dicho producto puede ser utilizado como parte de un sistema de seguridad activa dentro de un coche. El hecho de que el sistema se base en conjuntos de clasificadores permite una implementación distribuida del mismo lo que implica hardware económico e incremento de la tolerancia a fallos

La aplicación de modelos de consciencia artificial en los sistemas multiagente

Acta de: Campus Multidisciplinar en Percepción e Inteligencia, CMPI, 2006. Julio, 10-14, 2006. Albacete, España.Durante la última década han aparecido algunas implementaciones de modelos científicos de la consciencia basados en sistemas multiagente. El propósito de este artículo es recopilar y describir estos sistemas, determinando hasta que punto estas implementaciones satisfacen los modelos correspondientes, y analizando si proporcionan realmente las supuestas ventajas de usar consciencia artificial en la resolución de problemas. También se analizan en general las funciones de la consciencia y los beneficios que éstas pueden aportar en el rendimiento de los sistemas multiagente.Publicad

A machine consciousness approach to autonomous mobile robotics

Proceeding of: 5th International Cognitive Robotics Workshop, 2006 (The AAAI '06 Workshop on Cognitive Robotics). Boston, Massachusetts, USA, July 16-17, 2006.In this paper we argue that machine consciousness can be successfully modelled to be the base of a control system for an autonomous mobile robot. Such a bio-inspired system provides the robot with cognitive benefits the same way that consciousness does for humans and other higher mammals. The key functions of consciousness are identified and partially applied to an original computational model, which is implemented in a software simulated mobile robot. We use a simulator to prove our assumptions and gain insight about the benefits that conscious and affective functions add to the behaviour of the robot. A particular exploration problem is analyzed and experiments results are evaluated. We conclude that this cognitive approach involving consciousness and emotion functions cannot be ignored in the design of mobile robots, as it provides efficiency and robustness in autonomous tasks. Specifically, the proposed model has revealed efficient control behaviour when dealing with unexpected situations.Publicad

Applying machine consciousness models in autonomous situated agents

This paper briefly describes the most relevant current approaches to the implementation of scientific models of consciousness. Main aspects of scientific theories of consciousness are characterized in sight of their possible mapping into artificial implementations. These implementations are analyzed both theoretically and functionally. Also, a novel pragmatic functional approach to machine consciousness is proposed and discussed. A set of axioms for the presence of consciousness in agents is applied to evaluate and compare the various models

Learning sequences of rules using classifier systems with tags

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.The objective of this paper was to obtain an encoding structure that would allow the genetic evolution of rules in such a manner that the number of rules and relationship in a classifier system (CS) would be learnt in the evolution process. For this purpose, an area that allows the definition of rule groups has been entered into the condition and message part of the encoded rules. This area is called internal tag. This term was coined because the system has some similarities with natural processes that take place in certain animal species, where the existence of tags allows them to communicate and recognize each other. Such CS is called a tag classifier system (TCS). The TCS has been tested in the game of draughts and compared with the classical CS. The results show an improving of the CS performance

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.Publicad

Knowledge acquisition including tags in a classifier system

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.One of the major problems related to classifier systems is the loss of rules. This loss is caused by the genetic algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favours the generation of the stronger rules. When the learning system works in an environment in which it is possible to generate a complete training set, the strength of the rules of the CS will reflect the relative relationship between rules satisfactorily and, therefore, the application of the genetic algorithm will produce the desired effects. However, when the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favour of a given type of rules that would in turn be favoured by the genetic algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed classifier systems by means of a mechanism. Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. A modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations