Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Complete lattice projection autoassociative memories

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: A capacidade do cérebro humano de armazenar e recordar informações por associação tem inspirado o desenvolvimento de modelos matemáticos referidos na literatura como memórias associativas. Em primeiro lugar, esta tese apresenta um conjunto de memórias autoassociativas (AMs) que pertecem à ampla classe das memórias morfológicas autoassociativas (AMMs). Especificamente, as memórias morfológicas autoassociativas de projeção max-plus e min-plus (max-plus e min-plus PAMMs), bem como suas composições, são introduzidas nesta tese. Tais modelos podem ser vistos como versões não distribuídas das AMMs propostas por Ritter e Sussner. Em suma, a max-plus PAMM produz a maior combinação max-plus das memórias fundamentais que é menor ou igual ao padrão de entrada. Dualmente, a min-plus PAMM projeta o padrão de entrada no conjunto de todas combinações min-plus. Em segundo, no contexto da teoria dos conjuntos fuzzy, esta tese propõe novas memórias autoassociativas fuzzy, referidas como classe das max-C e min-D FPAMMs. Uma FPAMM representa uma rede neural morfológica fuzzy com uma camada oculta de neurônios que é concebida para o armazenamento e recordação de conjuntos fuzzy ou vetores num hipercubo. Experimentos computacionais relacionados à classificação de padrões e reconhecimento de faces indicam possíveis aplicações dos novos modelos acima mencionadosAbstract: The human brain¿s ability to store and recall information by association has inspired the development various mathematical models referred to in the literature as associative memories. Firstly, this thesis presents a set of autoassociative memories (AMs) that belong to the broad class of autoassociative morphological memories (AMMs). Specifically, the max-plus and min-plus projection autoassociative morphological memories (max-plus and min-plus PAMMs), as well as their compositions, are introduced in this thesis. These models are non-distributed versions of the AMM models developed by Ritter and Sussner. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input pattern. Dually, the min-plus PAMM projects the input pattern into the set of all min-plus combinations. In second, in the context of fuzzy set theory, this thesis proposes new fuzzy autoassociative memories mentioned as class of the max-C and min-D FPAMMs. A FPAMM represents a fuzzy morphological neural network with a hidden layer of neurons that is designed for the storage and retrieval of fuzzy sets or vectors on a hypercube. Computational experiments concerning pattern classification and face recognition indicate possible applications of the aforementioned new AM modelsDoutoradoMatematica AplicadaDoutor em Matemática AplicadaCAPE

Fuzzy logic control of automated guided vehicle

This thesis describes the fuzzy logic based control system for an automated guided vehicle ( AGV ) designed to navigate from one position and orientation to another while avoiding obstacles. A vehicle with an onboard computer system and a beacon based location system has been used to provide experimental confirmation of the methods proposed during this research. A simulation package has been written and used to test control techniques designed for the vehicle. A series of navigation rules based upon the vehicle's current position relative to its goal produce a fuzzy fit vector, the entries in which represent the relative importance of sets defined over all the possible output steering angles. This fuzzy fit vector is operated on by a new technique called rule spreading which ensures that all possible outputs have some activation. An obstacle avoidance controller operates from information about obstacles near to the vehicle. A method has been devised for generating obstacle avoidance sets depending on the size, shape and steering mechanism of a vehicle to enable their definition to accurately reflect the geometry and dynamic performance of the vehicle. Using a set of inhibitive rules the obstacle avoidance system compiles a mask vector which indicates the potential for a collision if each one of the possible output sets is chosen. The fuzzy fit vector is multiplied with the mask vector to produce a combined fit vector representing the relative importance of the output sets considering the demands of both navigation and obstacle avoidance. This is operated on by a newly developed windowing technique which prevents any conflicts produced by this combination leading to an undesirable output. The final fit vector is then defuzzified to give a demand steering angle for the vehicle. A separate fuzzy controller produces a demand velocity. In tests carried out in simulation and on the research vehicle it has been shown that the control system provides a successful guidance and obstacle avoidance scheme for an automated vehicle

Tunable equivalence fuzzy associative memories

This paper introduces a new class of fuzzy associative memories (FAMs) called tunable equivalence fuzzy associative memories, for short tunable E-FAMs or TE-FAMs, that are determined by the application of parametrized equivalence measures in the hidden nodes. Tunable E-FAMs belong to the class of Theta-FAMs that have recently appeared in the literature. In contrast to previous Theta-FAM models, tunable E-FAMs allow for the extraction of a fundamental memory set from the training data by means of an algorithm that depends on the evaluation of equivalence measures. Furthermore, we are able to optimize not only the weights corresponding to the contributions of the hidden nodes but also the contributions of the attributes of the data by tuning the parametrized equivalence measures used in a TE-FAM model. The computational effort involved in training tunable TE-FAMs is very low compared to the one of the previous Theta-FAM training algorithm. (C) 2015 Elsevier B.V. All rights reserved.This paper introduces a new class of fuzzy associative memories (FAMs) called tunable equivalence fuzzy associative memories, for short tunable E-FAMs or TE-FAMs, that are determined by the application of parametrized equivalence measures in the hidden nodes. Tunable E-FAMs belong to the class of Theta-FAMs that have recently appeared in the literature. In contrast to previous Theta-FAM models, tunable E-FAMs allow for the extraction of a fundamental memory set from the training data by means of an algorithm that depends on the evaluation of equivalence measures. Furthermore, we are able to optimize not only the weights corresponding to the contributions of the hidden nodes but also the contributions of the attributes of the data by tuning the parametrized equivalence measures used in a TE-FAM model. The computational effort involved in training tunable TE-FAMs is very low compared to the one of the previous Theta-FAM training algorithm.29224226

Theta-fuzzy associative memories (theta-fams)

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOMost fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, T-fuzzy associative memories (T-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of T-FAMs. In addition, we introduce a training algorithm for T-FAMs and we compare the error rates produced by T-FAMs and some well-known classifiers in some benchmark classification problems that are available on the internet. Finally, we apply T-FAMs to a problem of vision-based self-localization in mobile robotics.Most fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, Θ-fuzzy associative memories (Θ-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of Θ-FAMs. In addition, we introduce a training algorithm for Θ-FAMs and we compare the error rates produced by Θ-FAMs and some wellknown classifiers in some benchmark classification problems that are available on the internet. Finally, we apply Θ-FAMs to a problem of vision-based self-localization in mobile robotics.232313326FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO2009/16284-2; 2011/10014-

Tunable Equivalence Fuzzy Associative Memories

This paper introduces a new class of fuzzy associative memories (FAMs) called tunable equivalence fuzzy associative memories, for short tunable E-FAMs or TE-FAMs, that are determined by the application of parametrized equivalence measures in the hidden nodes. Tunable E-FAMs belong to the class of Theta-FAMs that have recently appeared in the literature. In contrast to previous Theta-FAM models, tunable E-FAMs allow for the extraction of a fundamental memory set from the training data by means of an algorithm that depends on the evaluation of equivalence measures. Furthermore, we are able to optimize not only the weights corresponding to the contributions of the hidden nodes but also the contributions of the attributes of the data by tuning the parametrized equivalence measures used in a TE-FAM model. The computational effort involved in training tunable TE-FAMs is very low compared to the one of the previous Theta-FAM training algorithm. (C) 2015 Elsevier B.V. All rights reserved.29224226

'Theta'-FAMs : fuzzy associative memories based on functions-'theta'

Orientador: Peter SussnerTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Muitas das memórias associativas fuzzy (FAMs) da literatura correspondem a redes neurais com uma única camada de pesos que armazenam de forma distributiva as informações das associações desejadas. As principais aplicações deste tipo de mémorias associativas são encontradas em sistemas baseados em regras fuzzy. Nesta tese introduzimos a classe de memórias associativas fuzzy-T (T-FAMs) que, em contraste com estes outros modelos, representam redes neurais fuzzy com duas camadas. Caso particulares de T-FAMs, denominadas S-FAMs (duais) e E-FAMs, são baseadas em medidas de subsethood e equivalência fuzzy. Resultados gerais sobre a capacidade de armazenamento e a capacidade de correção de erro das T-FAMs também foram providenciados. Adicionalmente, introduzimos um algoritmo geral de treinamento para T-FAM cuja convergência é sempre garantida. Apresentamos ainda um algoritmo alternativo para treinamento de uma certa classe de E-FAMs que além de ajustar os seus parâmetros também determina automaticamente a topologia da rede. Finalmente, comparamos as taxas de classificação produzidas pelas T-FAMs com alguns classificadores bem conhecidos em diversos problemas de classificação disponíveis na internet. Além disso, aplicamos com sucesso as T-FAMs em um problema de auto-localização de robô móvel baseado em visãoAbstract: Most fuzzy associative memories in the literature correspond to neural networks with a single layer of weights that distributively contains the information about the associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-base systems. In contrast, we present in this thesis the class of T-fuzzy associative memories (T-FAMs) that represent fuzzy neural networks with two layers. Particular cases of T-FAMs, called (dual) S-FAMs and E-FAMs, are based on fuzzy subsethood and equivalence measures. We provide theoretical results concerning the storage capability and error correction capability of T-FAMs. Furthermore, we introduce a general training algorithm for T-FAM that is guaranteed to converge in a finite numbers of iterations. We also proposed another alternative training algorithm for a certain type of E-FAM that not only adjusts the parameters of the corresponding network but also automatically determines its topology. We compare the classification rates produced by T-FAMs with that ones of some well-known classifiers in several benchmark classification problems that are available on the internet. Finally, we successful apply T-FAM approach to a problem of vision-based selflocalization in mobile roboticsDoutoradoMatematica AplicadaDoutor em Matemática Aplicad