<p>Taste, or gustation, has long been considered a primitive, and even non-rational, perceptual sense. Taste, as a subject of academic research, has been given very little attention; especially, when contrasted to other human perceptual senses. The knowledge of how people express and discuss their perceptions and sensations of tastes, and, specifically, the descriptions of tastes of chocolate, is very limited.</p><p>Furthermore, the terminological inconsistency in the vocabulary of chocolate tasting, with the risk of misunderstanding or miscommunication, suggests that a basic method for representing tastes is needed.</p><p>This thesis presents a study of how people actually express the perception or sensation of tasting, and specifically when tasting chocolate. This study also explores the possibility of crafting a method for use when describing the tastes of chocolate.</p><p>The study was carried out by holding two tasting workshops. The first one was concerned with recording conversations about tasting chocolate. Participants were asked to taste different kinds of chocolates and, freely, discuss what they perceived and sensed. In the second workshop the participants were asked to describe the tastes of chocolate using predetermined vocabulary and formatted questionnaires.</p><p>The results of this study are linguistic semantic analyses of the different words that were used, and also a proposal for a prototypical method to use when tasting chocolate.</p>

Taste, or gustation, has long been considered a primitive, and even non-rational, perceptual sense. Taste, as a subject of academic research, has been given very little attention; especially, when contrasted to other human perceptual senses. The knowledge of how people express and discuss their perceptions and sensations of tastes, and, specifically, the descriptions of tastes of chocolate, is very limited. Furthermore, the terminological inconsistency in the vocabulary of chocolate tasting, with the risk of misunderstanding or miscommunication, suggests that a basic method for representing tastes is needed. This thesis presents a study of how people actually express the perception or sensation of tasting, and specifically when tasting chocolate. This study also explores the possibility of crafting a method for use when describing the tastes of chocolate. The study was carried out by holding two tasting workshops. The first one was concerned with recording conversations about tasting chocolate. Participants were asked to taste different kinds of chocolates and, freely, discuss what they perceived and sensed. In the second workshop the participants were asked to describe the tastes of chocolate using predetermined vocabulary and formatted questionnaires. The results of this study are linguistic semantic analyses of the different words that were used, and also a proposal for a prototypical method to use when tasting chocolate.

Um dos principais desafios remanescentes em Inteligência Artificial é como fazer sistemas inteligentes ancorar abstrações de alto nível na realidade. Pelo menos parte do problema vai em direção da questão de qual arcabouço de representação é mais apropriado de maneira que facilite o reconhecimento de objetos. A cognição em animais, particularmente em humanos, pode dar pistas de como tal arcabouço de representação se parece. Estudos na cognição do reconhecimento de objetos sugere que o problema da representação na cognição incorpora informações a respeito de similaridade e informação holística-estrutural (i.e. todo-parte) a respeito de conceitos. Temos a visão de que sistemas computacionais que lidam com relações de todo-parte deveriam representar informações holístico-estruturais e similaridade. No entanto, não existe arcabouço de representação que permite tais informações serem representadas de forma integrada. Esta tese propõe um arcabouço de representação de informação de similaridade entre estruturas de todo-parte. Nossa proposta é baseada na teoria dos espaços conceituais. Estes são espaços matemáticos onde regiões e pontos representam conceitos e objetos respectivamente, tal que a distância entre estas entidades denota a sua similaridade. Na nossa proposta, todos e partes são representados em espaços conceituais distintos, chamados espaços holísticos e estruturais. Espaços holísticos permitem o julgamento de similaridade sistemático entre todos. Por outro lado, espaços estruturais permitem o julgamento de similaridade sistemático entre estrutura de partes. Um ponto em um espaço estrutural denota uma estrutura particular de partes; regiões neste espaço representam diferentes tipos de estruturas de parte. Através da redefinição de espaços conceituais como um produto de espaços holísticos e estruturais, nosso objetivo é permitir a representação de informações de similaridade entre diferentes todos, levando em consideração a similaridade entre partes compartilhadas e suas configurações. Esta tese tem três contribuições principais: uma teoria geral sobre espaços holísticos e estruturais; uma formalização da teoria fundada em produto de espaços métricos; e um algoritmo genérico para reconhecimento de objetos, implementando processamento holístico-estrutural. / One of main remaining challenges in Artificial Intelligence is how to make intelligent systems to ground high-level abstractions in reality. At least part of this problem comes down to the question of what representation framework is best suited in a way that facilitates object recognition. Animal cognition, particularly in humans, can give a clue of how such representation framework looks like. Studies on the cognition of object recognition suggests that representation in cognition incorporates similarity and holistic-structural (i.e. whole-part) information about concepts. We hold the view that computer systems dealing with part-whole relations should be able to represent similarity and holistic-structural information. However, there exists no representation framework that allows such information to be represented in an integrated way. This thesis proposes a concept representation framework for representing information about similarity between part-whole structures. We base our proposal on the theory of conceptual spaces, which are mathematical spaces where regions and points represent concepts and objects respectively, such that their distance denotes their similarity. In our proposal, parts and wholes are represented in distinct conceptual spaces called holistic and structure spaces. Holistic spaces allow for systematic similarity judgements between wholes. On the other hand, structure spaces allow for systematic similarity judgement between structures of parts. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. By redefining conceptual spaces as a product of holistic and structure spaces, our goal is to allow one to represent similarity information between different wholes, taking into consideration the similarity between shared parts and their configurations. This thesis has three main contributions: a general theory about holistic and structure spaces; a formulation of the theory founded on products of metric spaces; and a generic algorithm for object recognition implementing holistic-structural processing.

Um dos principais desafios remanescentes em Inteligência Artificial é como fazer sistemas inteligentes ancorar abstrações de alto nível na realidade. Pelo menos parte do problema vai em direção da questão de qual arcabouço de representação é mais apropriado de maneira que facilite o reconhecimento de objetos. A cognição em animais, particularmente em humanos, pode dar pistas de como tal arcabouço de representação se parece. Estudos na cognição do reconhecimento de objetos sugere que o problema da representação na cognição incorpora informações a respeito de similaridade e informação holística-estrutural (i.e. todo-parte) a respeito de conceitos. Temos a visão de que sistemas computacionais que lidam com relações de todo-parte deveriam representar informações holístico-estruturais e similaridade. No entanto, não existe arcabouço de representação que permite tais informações serem representadas de forma integrada. Esta tese propõe um arcabouço de representação de informação de similaridade entre estruturas de todo-parte. Nossa proposta é baseada na teoria dos espaços conceituais. Estes são espaços matemáticos onde regiões e pontos representam conceitos e objetos respectivamente, tal que a distância entre estas entidades denota a sua similaridade. Na nossa proposta, todos e partes são representados em espaços conceituais distintos, chamados espaços holísticos e estruturais. Espaços holísticos permitem o julgamento de similaridade sistemático entre todos. Por outro lado, espaços estruturais permitem o julgamento de similaridade sistemático entre estrutura de partes. Um ponto em um espaço estrutural denota uma estrutura particular de partes; regiões neste espaço representam diferentes tipos de estruturas de parte. Através da redefinição de espaços conceituais como um produto de espaços holísticos e estruturais, nosso objetivo é permitir a representação de informações de similaridade entre diferentes todos, levando em consideração a similaridade entre partes compartilhadas e suas configurações. Esta tese tem três contribuições principais: uma teoria geral sobre espaços holísticos e estruturais; uma formalização da teoria fundada em produto de espaços métricos; e um algoritmo genérico para reconhecimento de objetos, implementando processamento holístico-estrutural. / One of main remaining challenges in Artificial Intelligence is how to make intelligent systems to ground high-level abstractions in reality. At least part of this problem comes down to the question of what representation framework is best suited in a way that facilitates object recognition. Animal cognition, particularly in humans, can give a clue of how such representation framework looks like. Studies on the cognition of object recognition suggests that representation in cognition incorporates similarity and holistic-structural (i.e. whole-part) information about concepts. We hold the view that computer systems dealing with part-whole relations should be able to represent similarity and holistic-structural information. However, there exists no representation framework that allows such information to be represented in an integrated way. This thesis proposes a concept representation framework for representing information about similarity between part-whole structures. We base our proposal on the theory of conceptual spaces, which are mathematical spaces where regions and points represent concepts and objects respectively, such that their distance denotes their similarity. In our proposal, parts and wholes are represented in distinct conceptual spaces called holistic and structure spaces. Holistic spaces allow for systematic similarity judgements between wholes. On the other hand, structure spaces allow for systematic similarity judgement between structures of parts. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. By redefining conceptual spaces as a product of holistic and structure spaces, our goal is to allow one to represent similarity information between different wholes, taking into consideration the similarity between shared parts and their configurations. This thesis has three main contributions: a general theory about holistic and structure spaces; a formulation of the theory founded on products of metric spaces; and a generic algorithm for object recognition implementing holistic-structural processing.

Um dos principais desafios remanescentes em Inteligência Artificial é como fazer sistemas inteligentes ancorar abstrações de alto nível na realidade. Pelo menos parte do problema vai em direção da questão de qual arcabouço de representação é mais apropriado de maneira que facilite o reconhecimento de objetos. A cognição em animais, particularmente em humanos, pode dar pistas de como tal arcabouço de representação se parece. Estudos na cognição do reconhecimento de objetos sugere que o problema da representação na cognição incorpora informações a respeito de similaridade e informação holística-estrutural (i.e. todo-parte) a respeito de conceitos. Temos a visão de que sistemas computacionais que lidam com relações de todo-parte deveriam representar informações holístico-estruturais e similaridade. No entanto, não existe arcabouço de representação que permite tais informações serem representadas de forma integrada. Esta tese propõe um arcabouço de representação de informação de similaridade entre estruturas de todo-parte. Nossa proposta é baseada na teoria dos espaços conceituais. Estes são espaços matemáticos onde regiões e pontos representam conceitos e objetos respectivamente, tal que a distância entre estas entidades denota a sua similaridade. Na nossa proposta, todos e partes são representados em espaços conceituais distintos, chamados espaços holísticos e estruturais. Espaços holísticos permitem o julgamento de similaridade sistemático entre todos. Por outro lado, espaços estruturais permitem o julgamento de similaridade sistemático entre estrutura de partes. Um ponto em um espaço estrutural denota uma estrutura particular de partes; regiões neste espaço representam diferentes tipos de estruturas de parte. Através da redefinição de espaços conceituais como um produto de espaços holísticos e estruturais, nosso objetivo é permitir a representação de informações de similaridade entre diferentes todos, levando em consideração a similaridade entre partes compartilhadas e suas configurações. Esta tese tem três contribuições principais: uma teoria geral sobre espaços holísticos e estruturais; uma formalização da teoria fundada em produto de espaços métricos; e um algoritmo genérico para reconhecimento de objetos, implementando processamento holístico-estrutural. / One of main remaining challenges in Artificial Intelligence is how to make intelligent systems to ground high-level abstractions in reality. At least part of this problem comes down to the question of what representation framework is best suited in a way that facilitates object recognition. Animal cognition, particularly in humans, can give a clue of how such representation framework looks like. Studies on the cognition of object recognition suggests that representation in cognition incorporates similarity and holistic-structural (i.e. whole-part) information about concepts. We hold the view that computer systems dealing with part-whole relations should be able to represent similarity and holistic-structural information. However, there exists no representation framework that allows such information to be represented in an integrated way. This thesis proposes a concept representation framework for representing information about similarity between part-whole structures. We base our proposal on the theory of conceptual spaces, which are mathematical spaces where regions and points represent concepts and objects respectively, such that their distance denotes their similarity. In our proposal, parts and wholes are represented in distinct conceptual spaces called holistic and structure spaces. Holistic spaces allow for systematic similarity judgements between wholes. On the other hand, structure spaces allow for systematic similarity judgement between structures of parts. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. By redefining conceptual spaces as a product of holistic and structure spaces, our goal is to allow one to represent similarity information between different wholes, taking into consideration the similarity between shared parts and their configurations. This thesis has three main contributions: a general theory about holistic and structure spaces; a formulation of the theory founded on products of metric spaces; and a generic algorithm for object recognition implementing holistic-structural processing.

Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested.

An important capacity that is still lacking in intelligent systems such as robots, is the ability to use concepts in a human-like manner. Indeed, the use of concepts has been recognised as being fundamental to a wide range of cognitive skills, including classification, reasoning and memory. Intricately intertwined with language, concepts are at the core of human cognition; but despite a large body or research, their functioning is as of yet not well understood. Nevertheless it remains clear that if intelligent systems are to achieve a level of cognition comparable to humans, they will have to posses the ability to deal with the fundamental role that concepts play in cognition. A promising manner in which conceptual knowledge can be acquired by an intelligent system is through ongoing, incremental development. In this view, a system is situated in the world and gradually acquires skills and knowledge through interaction with its social and physical environment. Important in this regard is the notion that cognition is embodied. As such, both the physical body and the environment shape the manner in which cognition, including the learning and use of concepts, operates. Through active partaking in the interaction, an intelligent system might influence its learning experience as to be more effective. This work presents experiments which illustrate how these notions of interaction and embodiment can influence the learning process of artificial systems. It shows how an artificial agent can benefit from interactive learning. Rather than passively absorbing knowledge, the system actively partakes in its learning experience, yielding improved learning. Next, the influence of embodiment on perception is further explored in a case study concerning colour perception, which results in an alternative explanation for the question of why human colour experience is very similar amongst individuals despite physiological differences. Finally experiments, in which an artificial agent is embodied in a novel robot that is tailored for human-robot interaction, illustrate how active strategies are also beneficial in an HRI setting in which the robot learns from a human teacher.

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.

Natural Kinds, Causal Laws, Magical Concepts: an Approach to American Magical Realism”. Throughout Latin American history of ideas one can find an alienated relation towards nature, which appears as magical, indecipherable and hostile. The two main characteristics of this idea of nature are the abundance of strange species and the unpredictability of events. In this essay I will argue that the first characteristic is a natural effect of the process of inductive learning under the conditions of the discovery and conquest of America, and that the second characteristic is in turn an effect of the way how the empirical expectations with these strange species were cast in causal laws. / La historia de las ideas en América Latina está atravesada por una relación enajenada con la naturaleza, que aparece como mágica, indescifrable y hostil. Las dos características principales de esta concepción de naturaleza son la abundancia de especies extrañas y la impredecibilidad de los acontecimientos .En este ensayo argumentaré que la primera característica es un efecto natural del proceso de aprendizaje inductivo bajo condiciones del Descubrimiento y de la Conquista, y que la segunda característica es a su vez un efecto de la manera cómo las expectativas empíricas con las especies extrañas fueron cifradas en leyes causales.

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