Robot arm fuzzy control by a neuro-genetic algorithm

Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Parallel backpropagation neural networks forTask allocation by means of PVM

Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time. Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements. This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach. In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Robot arm fuzzy control by a neuro-genetic algorithm

Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A parallel approach for backpropagation learning of neural networks

Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed system architecture for developing parallel training with a partition pattern scheme. Under this approach, weight changes are computed concurrently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. Some comparative results are also shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony (AC) model for the Multiple Knapsack Problem (MKP). The ant colony metaphor, as well as other evolutionary metaphors, was applied successfully to diverse heavily constrained problems. An AC system is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an AC system is adapted to the MKP. We present some results regarding its performance against known optimum for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Facultad de Informátic

Parallel backpropagation neural networks forTask allocation by means of PVM

Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time. Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements. This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach. In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Balance de carga adaptativo basado en un sistema híbrido neuro-difuso para sistemas distribuidos de uso intensivo de CPU

La performance de un sistema distribuido puede ser mejorada si se utilizan estaciones de trabajo ociosas o con poca carga, para la ejecución de tareas que son asignadas inicialmente a estaciones de trabajo que no tienen capacidad suficiente. En este trabajo se presenta una estrategia para balance de carga en sistemas distribuidos para uso intensivo de CPU, basada en un sistema híbrido neuro-difuso. Se proveen además resultados experimentales obtenidos al evaluar la estrategia en un sistema distribuido simulado.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Balance de carga adaptativo basado en un sistema híbrido neuro-difuso para sistemas distribuidos de uso intensivo de CPU

La performance de un sistema distribuido puede ser mejorada si se utilizan estaciones de trabajo ociosas o con poca carga, para la ejecución de tareas que son asignadas inicialmente a estaciones de trabajo que no tienen capacidad suficiente. En este trabajo se presenta una estrategia para balance de carga en sistemas distribuidos para uso intensivo de CPU, basada en un sistema híbrido neuro-difuso. Se proveen además resultados experimentales obtenidos al evaluar la estrategia en un sistema distribuido simulado.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Reinforcement learning: Un estudio comparativo de la performance de sus principales métodos

En los últimos años, el interés por el concepto de Reinforcement Learning (RL) se ha incrementado en forma considerable dentro de la comunidad de investigadores de Machine Learning e Inteligencia Artificial en general. El principal motivo fue el suceso que los métodos de RL tuvieron en la resolución de problemas, que no lograban atacar en forma satisfactoria enfoques tradicionales como Programación Dinámica y aprendizaje supervisado (por ejemplo Redes Neuronales). RL ataca el problema de aprender a controlar agentes autónomos (como por ejemplo robots), mediante interacciones por prueba y error con un ambiente dinámico, el cual le provee señales de refuerzo por cada acción que realiza. La principal virtud de RL es que permite atacar el problema de la asignación de crédito temporal, el cual consiste en asignar un apropiado crédito o censura a las acciones individuales cuando el efecto o recompensa de dichas acciones es demorado hasta que una serie de acciones se han realizado. Los conceptos teóricos fundamentales de RL, como así también algunos de sus principales métodos son descriptos a modo de survey, dirigidos a aquellas personas que tienen interés en introducirse en este área. Se presenta un análisis comparativo de los resultados obtenidos mediante métodos libres de modelo (Q-Learning) y métodos que integran aprendizaje y planificación (Dyna-Q y Prioritized Sweeping), tomando como referencia los valores obtenidos con los métodos clásicos de Programación Dinámica (Value Iteration). También se analiza el problema conocido como el dilema de la exploración-explotación, ya que en RL es el agente quien controla la distribución de los ejemplos de entrenamiento, eligiendo la secuencia de acciones a tomar. Estos métodos se aplicaron a problemas del mundo de los laberintos, típicamente usados en el área.Sistemas Inteligentes - Sesión de póstersRed de Universidades con Carreras en Informática (RedUNCI