From the end of 80's there has been a great interest in the study of qualitative models to represent and to reason with spatial aspects. The present work is centred in the development and application of a model to reason about the shape and about the movement in a qualitative way, which means in a way similar to the human reasoning. The interest of this study is originated in the necessity of solutions for the recognition of objects and the description and reasoning about the movement in situations with high uncertainty, as it is the case of robotic applications, where robots only have limited and vague sensorial information. In these situations the use of a qualitative reasoning, that allows us to handle ambiguities and errors, will be the most suitable.The movement of an object can be considered as a shape whose topologic relation with its environment (considered as another shape) changes in the time. On the other hand the shape of the objects is a spatial aspect in itself, and again for its study we have used topological concepts. The recognition of objects is important during the movement of a robot since for the accomplishment of certain tasks the robot must be able to recognize the objects with which it is finding during its trajectory, since these objects can be landmarks or reference points that provides to the robot spatial information of its environment.Therefore this work will be centred in the study of three space aspects: the shape of the objects, the topology and the movement. Several works exist about the shape of the objects [Jungert 94; Park and Gero 99, 00; Chase 96, 97; Shokoufandeh, Dickinson et al. 02], on topology [Cohn, Bennet ET al. 97; Renz & Nebel 98; Egenhofer & Franzosa 91; Clementini & Di Felice 95] and on movement [Zimmermann and Freksa 93; Musto, Stein et al. 00; Musto et al. 99; Rajagopalan and Kuipers 94; Forbus 83; Muller 98a, 98b]. However, most of these works are theoretical and they have not been applied to robotics.This PhD thesis presents a motion model as a qualitative representational model for integrating qualitatively time and topological information for reasoning about dynamic worlds in which spatial relations between regions and between regions and objects may change with time. This qualitative integration of time and topology has been accomplished thanks to the definition of an approach with the following three steps: (1) the definition of the algebra of the spatial aspect to be integrated, which will be time and topology. The representation of each aspect is seen as an instance of the Constraint Satisfaction Problem (CSP); (2) the definition of the Basic Step of the Inference Process (BSIP) for each spatial aspect to be integrated. In general, the BSIP consists on given two relationships which relate three objects A, B, and C (one object is shared among the two relationships, for instance A is related with B and B is related with C), we will find the third relationship between objects A and C; and (3) the definition of the Full Inference Process (FIP) for each spatial aspect to be integrated which consists on repeating the BSIP as many times as possible with the initial information and the information provided by some BSIP, until no more information can be inferred.On the other hand, the theory for the recognition of shapes developed is able to describe several types of shapes, as they are regular and non-regular polygons, with or without holes, with or without curved segments and even completely curvilinear forms. The theory describes shapes considering qualitatively the angles, relative side length, concavities and convexities, and types of curvatures of their boundaries using only their relevant points, which are defined as vertices, and the initial, final point and point of maximum curvature of the curves. To describe shapes with holes, topological and qualitative spatial orientation aspects have been considered in order to relate the hole with its container. Each object is described by a string which describes its qualitative distinguished features (symbolic representation), which is used to match an object against the others. This theory has been applied, in an industrial domain, for the automatic and intelligent assembly of ceramic mosaics. Mosaics are made of pieces of different shapes, colours and sizes, named tesseraes, that once they are assembled they create a unique composition with high added value, due its artist and decorative value. Mosaics are made usually following a design describing the position of each tesserae in the final composition. The application developed in this dissertation, recognise individual tesseraes from pictures, which represent the tesserae coming over a conveyor, against a vectorial mosaic design. Therefore, the application returns the position of the tesserae in the mosaic together with the angle that a robot arm has to do when picking the tesserae by its centroid in order to leave it in the correct orientation inside the mosaic. On the other hand the simplest version of this theory, in concrete the part that describes regular and non-regular polygonal objects, jointly with the developed theory of movement has been applied too for the simulated navigation of a real robot, in concrete of the Khepera2 robot. This application consists in a world formed by two rooms connected by a corridor. The robot first learns the topological map of the world. Then in each room there is an object and the robot has to decide if both objects represent the same object or not, for that purpose the robot uses the movement theory to plan the way to do and to detect possible deviations during its moving, and finally by using the qualitative theory for shape matching developed decides if the objects has the same shape or not.

Em domínios com forte conteúdo visual, a interpretação de imagens por raciocínio visual pode ser mais eficaz na solução de problemas do que a interpretação de dados puramente textuais ou numéricos. No entanto, a representação do conhecimento visual é difícil de ser realizada por tratar-se de um conhecimento implícito para o observador. As ontologias de representação possibilitam a criação de estruturas para auxiliar na captura desse tipo de conhecimento, de forma a atribuir uma representação simbólica e significado semântico ao que está sendo visualizado. A formalização do conhecimento visual permite a sua utilização em processos de inferência, resultando na interpretação automática da imagem. O objetivo deste trabalho é a definição de construtos ontológicos que permitam descrever aspectos visuais presentes em uma imagem, com ênfase na atenção visual mais do que nos aspectos físicos dos objetos. Esses aspectos visuais são associados aos objetos físicos da imagem bem como aos objetos descritos no nível do conhecimento de domínio. Para cada um dos níveis foi definida uma ontologia de representação, sendo assim possível atribuir semântica específica a esses objetos através da descrição de seus atributos e manter a independência do conhecimento relativo a cada nível. O nível da imagem descreve os objetos passíveis de serem extraídos por algoritmos de processamento de imagem (embora esses algoritmos não tenham sido foco de estudo neste trabalho). O nível visual descreve objetos que são foco da atenção visual, tais como seções, interstícios e contornos. O nível semântico descreve os objetos da aplicação capturados através de aquisição de conhecimento. A identidade dos objetos modelados é garantida através de relações de mapeamento entre cada dois níveis adjacentes. O domínio de aplicação deste trabalho foi a Petrografia Sedimentar, com o objetivo de extrair por inferência a qualidade em termos de porosidade e permeabilidade de rochas reservatório de petróleo. Com ajuda do especialista, foi modelado um método de solução de problemas para identificação do grau de compactação da rocha, que raciocina sobre os conhecimentos modelados utilizando a ontologia proposta. Foi implementado um sistema que permite a descrição dos objetos individualizados através da segmentação manual da imagem, mapeando os dados descritos para a ontologia e aplicando sobre ela o método de solução de problemas. Esse sistema gera como resultado o grau de compactação da rocha, cuja imagem foi assim descrita. Uma validação preliminar da abordagem foi realizada através da descrição de imagens de rochas fazendo uso do sistema desenvolvido, confrontando os resultados com os obtidos por um geólogo para as mesmas rochas observadas. Na metade das amostras descritas, o sistema atingiu o mesmo resultado do especialista e, na outra metade, obteve grande aproximação dos resultados. / In domains that have strong visual content, the image interpretation applying visual reasoning can be more effective in solving problems than the interpretation of pure textual or numeric data. However, the representation of visual knowledge is hard to be achieved since, most of time, we are dealing with implicit knowledge for the observer. The representation ontologies allow the creation of structures for assisting the capture of this kind of knowledge, in order to associate a symbolic representation and semantic meaning to what it being visualize. The formalization of the visual knowledge allows its application for inference process, resulting in the automatic interpretation of image. The goal of this work is the definition of ontological constructs that allow describing the visual aspects presented in an image, giving more emphasis in the evidences captured by visual attention than in the physical aspects of the objects. These aspects are associated to the physical objects as well as to the objects described in the domain knowledge level. Separate representation ontologies were defined for each level, making possible to associate specific semantic content to the objects through the description of the attributes and to keep the independence of the knowledge related to each level. The image level describes the objects that are possible of being extracted by image processing algorithms (although these algorithms were not studied in this work). The visual knowledge describes the objects that capture the visual attention, such as sections, interstices and borders. The semantic level describes the application objects elicited by knowledge acquisition methods. The identity of the modeled objects is guaranteed through the mapping relation defined between each two adjacent levels. The application domain of this work is the Sedimentary Petrography, with the goal of extracting by inference methods the porosity and permeability quality of petroleum reservoir-rocks. With the aid of the expert, a problem-solving method that reasons over the knowledge formalized through the proposed ontology was modeled for the identification of the compaction level of the rock. Furthermore, it was implemented a system that supports the description of the objects individualized through a manual segmentation of the image. The described data was mapped to the ontology and the problem-solving method was applied to define the level of compaction. A preliminary validation was developed comparing the results achieved by the system with the manual interpretation done by the expert with the same rock samples. With the half of the described samples the system achieved the same results of the expert and has got strong approximation in the other half.

Em domínios com forte conteúdo visual, a interpretação de imagens por raciocínio visual pode ser mais eficaz na solução de problemas do que a interpretação de dados puramente textuais ou numéricos. No entanto, a representação do conhecimento visual é difícil de ser realizada por tratar-se de um conhecimento implícito para o observador. As ontologias de representação possibilitam a criação de estruturas para auxiliar na captura desse tipo de conhecimento, de forma a atribuir uma representação simbólica e significado semântico ao que está sendo visualizado. A formalização do conhecimento visual permite a sua utilização em processos de inferência, resultando na interpretação automática da imagem. O objetivo deste trabalho é a definição de construtos ontológicos que permitam descrever aspectos visuais presentes em uma imagem, com ênfase na atenção visual mais do que nos aspectos físicos dos objetos. Esses aspectos visuais são associados aos objetos físicos da imagem bem como aos objetos descritos no nível do conhecimento de domínio. Para cada um dos níveis foi definida uma ontologia de representação, sendo assim possível atribuir semântica específica a esses objetos através da descrição de seus atributos e manter a independência do conhecimento relativo a cada nível. O nível da imagem descreve os objetos passíveis de serem extraídos por algoritmos de processamento de imagem (embora esses algoritmos não tenham sido foco de estudo neste trabalho). O nível visual descreve objetos que são foco da atenção visual, tais como seções, interstícios e contornos. O nível semântico descreve os objetos da aplicação capturados através de aquisição de conhecimento. A identidade dos objetos modelados é garantida através de relações de mapeamento entre cada dois níveis adjacentes. O domínio de aplicação deste trabalho foi a Petrografia Sedimentar, com o objetivo de extrair por inferência a qualidade em termos de porosidade e permeabilidade de rochas reservatório de petróleo. Com ajuda do especialista, foi modelado um método de solução de problemas para identificação do grau de compactação da rocha, que raciocina sobre os conhecimentos modelados utilizando a ontologia proposta. Foi implementado um sistema que permite a descrição dos objetos individualizados através da segmentação manual da imagem, mapeando os dados descritos para a ontologia e aplicando sobre ela o método de solução de problemas. Esse sistema gera como resultado o grau de compactação da rocha, cuja imagem foi assim descrita. Uma validação preliminar da abordagem foi realizada através da descrição de imagens de rochas fazendo uso do sistema desenvolvido, confrontando os resultados com os obtidos por um geólogo para as mesmas rochas observadas. Na metade das amostras descritas, o sistema atingiu o mesmo resultado do especialista e, na outra metade, obteve grande aproximação dos resultados. / In domains that have strong visual content, the image interpretation applying visual reasoning can be more effective in solving problems than the interpretation of pure textual or numeric data. However, the representation of visual knowledge is hard to be achieved since, most of time, we are dealing with implicit knowledge for the observer. The representation ontologies allow the creation of structures for assisting the capture of this kind of knowledge, in order to associate a symbolic representation and semantic meaning to what it being visualize. The formalization of the visual knowledge allows its application for inference process, resulting in the automatic interpretation of image. The goal of this work is the definition of ontological constructs that allow describing the visual aspects presented in an image, giving more emphasis in the evidences captured by visual attention than in the physical aspects of the objects. These aspects are associated to the physical objects as well as to the objects described in the domain knowledge level. Separate representation ontologies were defined for each level, making possible to associate specific semantic content to the objects through the description of the attributes and to keep the independence of the knowledge related to each level. The image level describes the objects that are possible of being extracted by image processing algorithms (although these algorithms were not studied in this work). The visual knowledge describes the objects that capture the visual attention, such as sections, interstices and borders. The semantic level describes the application objects elicited by knowledge acquisition methods. The identity of the modeled objects is guaranteed through the mapping relation defined between each two adjacent levels. The application domain of this work is the Sedimentary Petrography, with the goal of extracting by inference methods the porosity and permeability quality of petroleum reservoir-rocks. With the aid of the expert, a problem-solving method that reasons over the knowledge formalized through the proposed ontology was modeled for the identification of the compaction level of the rock. Furthermore, it was implemented a system that supports the description of the objects individualized through a manual segmentation of the image. The described data was mapped to the ontology and the problem-solving method was applied to define the level of compaction. A preliminary validation was developed comparing the results achieved by the system with the manual interpretation done by the expert with the same rock samples. With the half of the described samples the system achieved the same results of the expert and has got strong approximation in the other half.

Em domínios com forte conteúdo visual, a interpretação de imagens por raciocínio visual pode ser mais eficaz na solução de problemas do que a interpretação de dados puramente textuais ou numéricos. No entanto, a representação do conhecimento visual é difícil de ser realizada por tratar-se de um conhecimento implícito para o observador. As ontologias de representação possibilitam a criação de estruturas para auxiliar na captura desse tipo de conhecimento, de forma a atribuir uma representação simbólica e significado semântico ao que está sendo visualizado. A formalização do conhecimento visual permite a sua utilização em processos de inferência, resultando na interpretação automática da imagem. O objetivo deste trabalho é a definição de construtos ontológicos que permitam descrever aspectos visuais presentes em uma imagem, com ênfase na atenção visual mais do que nos aspectos físicos dos objetos. Esses aspectos visuais são associados aos objetos físicos da imagem bem como aos objetos descritos no nível do conhecimento de domínio. Para cada um dos níveis foi definida uma ontologia de representação, sendo assim possível atribuir semântica específica a esses objetos através da descrição de seus atributos e manter a independência do conhecimento relativo a cada nível. O nível da imagem descreve os objetos passíveis de serem extraídos por algoritmos de processamento de imagem (embora esses algoritmos não tenham sido foco de estudo neste trabalho). O nível visual descreve objetos que são foco da atenção visual, tais como seções, interstícios e contornos. O nível semântico descreve os objetos da aplicação capturados através de aquisição de conhecimento. A identidade dos objetos modelados é garantida através de relações de mapeamento entre cada dois níveis adjacentes. O domínio de aplicação deste trabalho foi a Petrografia Sedimentar, com o objetivo de extrair por inferência a qualidade em termos de porosidade e permeabilidade de rochas reservatório de petróleo. Com ajuda do especialista, foi modelado um método de solução de problemas para identificação do grau de compactação da rocha, que raciocina sobre os conhecimentos modelados utilizando a ontologia proposta. Foi implementado um sistema que permite a descrição dos objetos individualizados através da segmentação manual da imagem, mapeando os dados descritos para a ontologia e aplicando sobre ela o método de solução de problemas. Esse sistema gera como resultado o grau de compactação da rocha, cuja imagem foi assim descrita. Uma validação preliminar da abordagem foi realizada através da descrição de imagens de rochas fazendo uso do sistema desenvolvido, confrontando os resultados com os obtidos por um geólogo para as mesmas rochas observadas. Na metade das amostras descritas, o sistema atingiu o mesmo resultado do especialista e, na outra metade, obteve grande aproximação dos resultados. / In domains that have strong visual content, the image interpretation applying visual reasoning can be more effective in solving problems than the interpretation of pure textual or numeric data. However, the representation of visual knowledge is hard to be achieved since, most of time, we are dealing with implicit knowledge for the observer. The representation ontologies allow the creation of structures for assisting the capture of this kind of knowledge, in order to associate a symbolic representation and semantic meaning to what it being visualize. The formalization of the visual knowledge allows its application for inference process, resulting in the automatic interpretation of image. The goal of this work is the definition of ontological constructs that allow describing the visual aspects presented in an image, giving more emphasis in the evidences captured by visual attention than in the physical aspects of the objects. These aspects are associated to the physical objects as well as to the objects described in the domain knowledge level. Separate representation ontologies were defined for each level, making possible to associate specific semantic content to the objects through the description of the attributes and to keep the independence of the knowledge related to each level. The image level describes the objects that are possible of being extracted by image processing algorithms (although these algorithms were not studied in this work). The visual knowledge describes the objects that capture the visual attention, such as sections, interstices and borders. The semantic level describes the application objects elicited by knowledge acquisition methods. The identity of the modeled objects is guaranteed through the mapping relation defined between each two adjacent levels. The application domain of this work is the Sedimentary Petrography, with the goal of extracting by inference methods the porosity and permeability quality of petroleum reservoir-rocks. With the aid of the expert, a problem-solving method that reasons over the knowledge formalized through the proposed ontology was modeled for the identification of the compaction level of the rock. Furthermore, it was implemented a system that supports the description of the objects individualized through a manual segmentation of the image. The described data was mapped to the ontology and the problem-solving method was applied to define the level of compaction. A preliminary validation was developed comparing the results achieved by the system with the manual interpretation done by the expert with the same rock samples. With the half of the described samples the system achieved the same results of the expert and has got strong approximation in the other half.

Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn about the world around it. Although experience is fundamental to learning, it is costly and time-consuming to obtain. In order to speed this process up, humans in particular have developed communication abilities so that ideas and knowledge can be shared without requiring first-hand experience. Consider the same need for knowledge sharing among robots. Based on the recent growth of the field, it is reasonable to assume that in the near future there will be a collection of robots learning to perform tasks and gaining their own experiences in the world. In order to speed this learning up, it would be beneficial for the various robots to share their knowledge with each other. In most cases, however, the communication of knowledge among humans relies on the existence of similar sensory and motor capabilities. Robots, on the other hand, widely vary in perceptual and motor apparatus, ranging from simple light sensors to sophisticated laser and vision sensing. This dissertation defines the problem of how heterogeneous robots with widely different capabilities can share experiences gained in the world in order to speed up learning. The work focus specifically on differences in sensing and perception, which can be used both for perceptual categorization tasks as well as determining actions based on environmental features. Motivating the problem, experiments first demonstrate that heterogeneity does indeed pose a problem during the transfer of object models from one robot to another. This is true even when using state of the art object recognition algorithms that use SIFT features, designed to be unique and reproducible. It is then shown that the abstraction of raw sensory data into intermediate categories for multiple object features (such as color, texture, shape, etc.), represented as Gaussian Mixture Models, can alleviate some of these issues and facilitate effective knowledge transfer. Object representation, heterogeneity, and knowledge transfer is framed within Gärdenfors' conceptual spaces, or geometric spaces that utilize similarity measures as the basis of categorization. This representation is used to model object properties (e.g. color or texture) and concepts (object categories and specific objects). A framework is then proposed to allow heterogeneous robots to build models of their differences with respect to the intermediate representation using joint interaction in the environment. Confusion matrices are used to map property pairs between two heterogeneous robots, and an information-theoretic metric is proposed to model information loss when going from one robot's representation to another. We demonstrate that these metrics allow for cognizant failure, where the robots can ascertain if concepts can or cannot be shared, given their respective capabilities. After this period of joint interaction, the learned models are used to facilitate communication and knowledge transfer in a manner that is sensitive to the robots' differences. It is shown that heterogeneous robots are able to learn accurate models of their similarities and difference, and to use these models to transfer learned concepts from one robot to another in order to bootstrap the learning of the receiving robot. In addition, several types of communication tasks are used in the experiments. For example, how can a robot communicate a distinguishing property of an object to help another robot differentiate it from its surroundings? Throughout the dissertation, the claims will be validated through both simulation and real-robot experiments.

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