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Peter Gärdenfors - One of the best experts on this subject based on the ideXlab platform.
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Using Conceptual Spaces to exhibit Conceptual continuity through scientific theory change
European Journal for Philosophy of Science, 2017Co-Authors: George Masterton, Frank Zenker, Peter GärdenforsAbstract:There is a great deal of justified concern about continuity through scientific theory change. Our thesis is that, particularly in physics, such continuity can be appropriately captured at the level of Conceptual frameworks (the level above the theories themselves) using Conceptual Space models. Indeed, we contend that the Conceptual Spaces of three of our most important physical theories—Classical Mechanics (CM), Special Relativity Theory (SRT), and Quantum Mechanics (QM)—have already been so modelled as phase-Spaces. Working with their phase-Space formulations, one can trace the Conceptual changes and continuities in transitioning from CM to QM, and from CM to SRT. By offering a revised severity-ordering of changes that Conceptual frameworks can undergo, we provide reasons to doubt the commonly held view that CM is Conceptually closer to SRT than QM.
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modeling diachronic changes in structuralism and in Conceptual Spaces
Erkenntnis, 2014Co-Authors: Frank Zenker, Peter GärdenforsAbstract:Our aim in this article is to show how the theory of Conceptual Spaces can be useful in describing diachronic changes to Conceptual frameworks, and thus useful in understanding Conceptual change in the empirical sciences. We also compare the Conceptual Space approach to Moulines’s typology of intertheoretical relations in the structuralist tradition. Unlike structuralist reconstructions, those based on Conceptual Spaces yield a natural way of modeling the changes of a Conceptual framework, including noncumulative changes, by tracing the changes to the dimensions that reconstitute a Conceptual framework. As a consequence, the incommensurability of empirical theories need not be viewed as a matter of Conceptual representation.
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Modeling Diachronic Changes in Structuralism and in Conceptual Spaces
Erkenntnis, 2013Co-Authors: Frank Zenker, Peter GärdenforsAbstract:Abstract in Undetermined Our aim in this article is to show how the theory of Conceptual Spaces can be useful in describing diachronic changes to Conceptual frameworks, and thus useful in understanding Conceptual change in the empirical sciences. We also compare the Conceptual Space approach to Moulines's typology of intertheoretical relations in the structuralist tradition. Unlike structuralist reconstructions, those based on Conceptual Spaces yield a natural way of modeling the changes of a Conceptual framework, including noncumulative changes, by tracing the changes to the dimensions that reconstitute a Conceptual framework. As a consequence, the incommensurability of empirical theories need not be viewed as a matter of Conceptual representation
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IJCAI - Reasoning about categories in Conceptual Spaces
2001Co-Authors: Peter Gärdenfors, Mary-anne WilliamsAbstract:Understanding the process of categorization is a primary research goal in artificial intelligence. The Conceptual Space framework provides a flexible approach to modeling context-sensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in Conceptual Spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in Conceptual Spaces, and we show how the Region Connection Calculus can be used to reason about categories and other Conceptual regions.
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Three levels of inductive inference
Studies in logic and the foundations of mathematics, 1995Co-Authors: Peter GärdenforsAbstract:Publisher Summary This chapter describes the three levels of inductive inference. One of the most impressive features of human cognitive processing is the ability to perform inductive inferences. The three levels of accounting for observations are: the linguistic, the Conceptual, and the subConceptual level. In the linguistic level, the way of viewing observations consists of describing them in some specified language. The language is assumed to be equipped with a fixed set of primitive predicates and the denotations of these predicates are taken to be known. In the Conceptual level, observations are not defined in relation to some language, but characterized in terms of some underlying “Conceptual Space.” The Conceptual Space, which is more or less connected to perceptual mechanisms, consists of a number of “quality dimensions.” In the subConceptual level, observations are characterized in terms of inputs from sensory receptors. The observations are thus described as occurring before Conceptualization. This chapter argues that depending on which approach to observations is adopted, thoroughly different considerations about inductive inferences will come into focus.
Giovanni Pilato - One of the best experts on this subject based on the ideXlab platform.
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BICA - A New Humanoid Architecture for Social Interaction between Human and a Robot Expressing Human-Like Emotions Using an Android Mobile Device as Interface
Biologically Inspired Cognitive Architectures 2012, 2013Co-Authors: Antonio Chella, Rosario Sorbello, Giovanni Pilato, Giorgio Vassallo, Marcello GiardinaAbstract:In this paper we illustrate a humanoid robot able to interact socially and naturally with a human by expressing human-like body emotions. The emotional architecture of this robot is based on an emotional Conceptual Space generated using the paradigm of Latent Semantic Analysis. The robot generates its overall affective behavior (Latent Semantic Behavior) taking into account the visual and phrasal stimuli of human user, the environment and its personality, all encoded in his emotional Conceptual Space. The robot determines its emotion according by all these parameters that influence and orient the generation of his behavior not predictable from the user. The goal of this approach is to obtain an affinity matching with humans. The robot exhibit a smoothly natural transition in his emotion changes during the interaction with humans taking also into account the previous generated emotions. To validate the system, we implemented the distribute system on an Aldebaran NAO small humanoid robot and on a Android Phone HTC and we tested this social emotional interaction using the phone device as intelligent interface between human and robot in a complex scenario.
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AIC@AI*IA - Acting on Conceptual Spaces in Cognitive Agents.
2013Co-Authors: Agnese Augello, Salvatore Gaglio, Gianluigi Oliveri, Giovanni PilatoAbstract:Conceptual Spaces were originally introduced by Gardenfors as a bridge between symbolic and connectionist models of information representation. In our opinion, a cognitive agent, besides being able to work within his (current) Conceptual Space, must also be able to ‘produce a new Space’ by means of ‘global’ operations. These are operations which, acting on a Conceptual Space taken as a whole, generate other Conceptual Spaces.
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AI*IA - An Architecture with a Mobile Phone Interface for the Interaction of a Human with a Humanoid Robot Expressing Emotions and Personality
AI*IA 2011: Artificial Intelligence Around Man and Beyond, 2011Co-Authors: Antonio Chella, Rosario Sorbello, Giovanni Pilato, Giorgio Vassallo, Giuseppe Balistreri, Marcello GiardinaAbstract:In this paper is illustrated the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. We developed an architecture based on three main areas: Sub-Conceptual, Emotional and Behavioral. The first area analyzes perceptual data coming from the sensors. The second area builds the sub-symbolic representation of emotions in a Conceptual Space of emotional states. The last area triggers a latent semantic behavior which is related to the humanoid emotional state. The robot shows its overall behavior also taking into account its "personality". We implemented the system on a Aldebaran NAO humanoid robot and we tested the emotional interaction with humans through the use of a mobile phone as an interface.
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an architecture for humanoid robot expressing emotions and personality
Biologically Inspired Cognitive Architectures, 2010Co-Authors: Antonio Chella, Rosario Sorbello, Giorgio Vassallo, Giovanni PilatoAbstract:In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-Conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “Conceptual Space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the humanoid emotional state. The robot generates its overall behavior also taking into account its “personality”. To validate the system, we implemented the system on a Aldebaran NAO humanoid robot.
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BICA - An Architecture For Humanoid Robot Expressing Emotions And Personality
2010Co-Authors: Antonio Chella, Rosario Sorbello, Giorgio Vassallo, Giovanni PilatoAbstract:In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-Conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “Conceptual Space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the humanoid emotional state. The robot generates its overall behavior also taking into account its “personality”. To validate the system, we implemented the system on a Aldebaran NAO humanoid robot.
Hanzi Wang - One of the best experts on this subject based on the ideXlab platform.
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Conceptual Space based model fitting for multi-structure data
Neurocomputing, 2018Co-Authors: Guobao Xiao, Xing Wang, Jin Zheng, Yan Yan, Hailing Luo, Hanzi WangAbstract:Abstract In this paper, we propose a novel fitting method, called the Conceptual Space based Model Fitting (CSMF), to fit and segment multi-structure data contaminated with a large number of outliers. CSMF includes two main parts: an outlier removal algorithm and a model selection algorithm. Specifically, we firstly construct a novel Conceptual Space to measure data points by only considering the good model hypotheses. Then we analyze the Conceptual Space to effectively remove the gross outliers. Based on the results of outlier removal, we propose to search center points (representing the estimated model instances) in the Conceptual Space for model selection. CSMF is able to efficiently and effectively remove gross outliers in data, and simultaneously estimate the number and the parameters of model instances without using prior information. Experimental results on both synthetic data and real images demonstrate the advantages of the proposed method over several state-of-the-art fitting methods.
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Conceptual Space based gross outlier removal for geometric model fitting
International Conference on Control Automation Robotics and Vision, 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a Conceptual Space by computing the preference of "good" model hypotheses. In the Conceptual Space, the distributions of inliers and gross outliers are significantly different. Specifically, inliers of each model instance are distributed in a subSpace and they are far away from the origin of the Conceptual Space, while gross outliers are distributed near the origin. In this manner, the problem of densely gross outlier removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of outliers and effectively remove gross outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
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ICARCV - Conceptual Space based gross outlier removal for geometric model fitting
2016 14th International Conference on Control Automation Robotics and Vision (ICARCV), 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a Conceptual Space by computing the preference of "good" model hypotheses. In the Conceptual Space, the distributions of inliers and gross outliers are significantly different. Specifically, inliers of each model instance are distributed in a subSpace and they are far away from the origin of the Conceptual Space, while gross outliers are distributed near the origin. In this manner, the problem of densely gross outlier removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of outliers and effectively remove gross outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
Antonio Chella - One of the best experts on this subject based on the ideXlab platform.
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a cognitive architecture for music perception exploiting Conceptual Spaces
2015Co-Authors: Antonio ChellaAbstract:A cognitive architecture for a musical agent is presented. The architecture extends and complete an architecture for computer vision previously developed by the author by taking into account many relationships between vision and music perception. The focus of the agent architecture is an intermediate Conceptual area between the subConceptual and linguistic areas. A Conceptual Space for the perception of tones and intervals is thus presented, based on the dissonance measure of the tones. Problems and future works of the proposed approach are finally discussed.
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BICA - A New Humanoid Architecture for Social Interaction between Human and a Robot Expressing Human-Like Emotions Using an Android Mobile Device as Interface
Biologically Inspired Cognitive Architectures 2012, 2013Co-Authors: Antonio Chella, Rosario Sorbello, Giovanni Pilato, Giorgio Vassallo, Marcello GiardinaAbstract:In this paper we illustrate a humanoid robot able to interact socially and naturally with a human by expressing human-like body emotions. The emotional architecture of this robot is based on an emotional Conceptual Space generated using the paradigm of Latent Semantic Analysis. The robot generates its overall affective behavior (Latent Semantic Behavior) taking into account the visual and phrasal stimuli of human user, the environment and its personality, all encoded in his emotional Conceptual Space. The robot determines its emotion according by all these parameters that influence and orient the generation of his behavior not predictable from the user. The goal of this approach is to obtain an affinity matching with humans. The robot exhibit a smoothly natural transition in his emotion changes during the interaction with humans taking also into account the previous generated emotions. To validate the system, we implemented the distribute system on an Aldebaran NAO small humanoid robot and on a Android Phone HTC and we tested this social emotional interaction using the phone device as intelligent interface between human and robot in a complex scenario.
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AI*IA - An Architecture with a Mobile Phone Interface for the Interaction of a Human with a Humanoid Robot Expressing Emotions and Personality
AI*IA 2011: Artificial Intelligence Around Man and Beyond, 2011Co-Authors: Antonio Chella, Rosario Sorbello, Giovanni Pilato, Giorgio Vassallo, Giuseppe Balistreri, Marcello GiardinaAbstract:In this paper is illustrated the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. We developed an architecture based on three main areas: Sub-Conceptual, Emotional and Behavioral. The first area analyzes perceptual data coming from the sensors. The second area builds the sub-symbolic representation of emotions in a Conceptual Space of emotional states. The last area triggers a latent semantic behavior which is related to the humanoid emotional state. The robot shows its overall behavior also taking into account its "personality". We implemented the system on a Aldebaran NAO humanoid robot and we tested the emotional interaction with humans through the use of a mobile phone as an interface.
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an architecture for humanoid robot expressing emotions and personality
Biologically Inspired Cognitive Architectures, 2010Co-Authors: Antonio Chella, Rosario Sorbello, Giorgio Vassallo, Giovanni PilatoAbstract:In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-Conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “Conceptual Space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the humanoid emotional state. The robot generates its overall behavior also taking into account its “personality”. To validate the system, we implemented the system on a Aldebaran NAO humanoid robot.
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BICA - An Architecture For Humanoid Robot Expressing Emotions And Personality
2010Co-Authors: Antonio Chella, Rosario Sorbello, Giorgio Vassallo, Giovanni PilatoAbstract:In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional Conceptual Space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-Conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “Conceptual Space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the humanoid emotional state. The robot generates its overall behavior also taking into account its “personality”. To validate the system, we implemented the system on a Aldebaran NAO humanoid robot.
Steven Schockaert - One of the best experts on this subject based on the ideXlab platform.
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learning Conceptual Space representations of interrelated concepts
International Joint Conference on Artificial Intelligence, 2018Co-Authors: Zied Bouraoui, Steven SchockaertAbstract:Several recently proposed methods aim to learn Conceptual Space representations from large text collections. These learned representations associate each object from a given domain of interest with a point in a high-dimensional Euclidean Space, but they do not model the concepts from this domain, and can thus not directly be used for categorization and related cognitive tasks. A natural solution is to represent concepts as Gaussians, learned from the representations of their instances, but this can only be reliably done if sufficiently many instances are given, which is often not the case. In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other. We show that this leads to substantially better predictions in a knowledge base completion task.
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Inducing semantic relations from Conceptual Spaces
Artificial Intelligence, 2015Co-Authors: Joaquín Derrac, Steven SchockaertAbstract:Commonsense reasoning patterns such as interpolation and a fortiori inference have proven useful for dealing with gaps in structured knowledge bases. An important difficulty in applying these reasoning patterns in practice is that they rely on fine-grained knowledge of how different concepts and entities are semantically related. In this paper, we show how the required semantic relations can be learned from a large collection of text documents. To this end, we first induce a Conceptual Space from the text documents, using multi-dimensional scaling. We then rely on the key insight that the required semantic relations correspond to qualitative spatial relations in this Conceptual Space. Among others, in an entirely unsupervised way, we identify salient directions in the Conceptual Space which correspond to interpretable relative properties such as 'more fruity than' (in a Space of wines), resulting in a symbolic and interpretable representation of the Conceptual Space. To evaluate the quality of our semantic relations, we show how they can be exploited by a number of commonsense reasoning based classifiers. We experimentally show that these classifiers can outperform standard approaches, while being able to provide intuitive explanations of classification decisions. A number of crowdsourcing experiments provide further insights into the nature of the extracted semantic relations.
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ECAI - Characterising semantic relatedness using interpretable directions in Conceptual Spaces
2014Co-Authors: Joaquín Derrac, Steven SchockaertAbstract:Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a Conceptual Space. In particular, we use multi-dimensional scaling to induce a Conceptual Space from a relevant text corpus and then identify directions that correspond to relative properties such as "more violent than" in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning.