During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...) also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G¨ardenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by G¨ardenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs. (shrink)

And why is there a subjective component to experience?). It is easy to see that the separation between Weak and Strong Artificial Consciousness mirrors the separation between the easy problems and the hard problems of consciousness.

Machine consciousness is not only a technological challenge, but a new way to approach scientific and theoretical issues which have not yet received a satisfactory solution from AI and robotics. We outline the foundations and the objectives of machine consciousness from the standpoint of building a conscious robot.

The present paper aims to validate our research on human–humanoid interaction (HHI) using the minimalist humanoid robot Telenoid. We conducted the human–robot interaction test with 142 young people who had no prior interaction experience with this robot. The main goal is the analysis of the two social dimensions (‘‘Perception’’ and ‘‘Believability’’) useful for increasing the natural behaviour between users and Telenoid.Weadministered our custom questionnaire to human subjects in association with a well defined experimental setting (‘‘ordinary and goal-guided task’’). A thorough (...) analysis of the questionnaires has been carried out and reliability and internal consistency in correlation between the multiple items has been calculated. Our experimental results show that the perceptual behaviour and believability, as implicit social competences, could improve the meaningfulness and the natural-like sense of human–humanoid interaction in everyday life task-driven activities. Telenoid is perceived as an autonomous cooperative agent for a shared environment by human beings. (shrink)

Imitation in robotics is seen as a powerful means to reduce the complexity of robot programming. It allows users to instruct robots by simply showing them how to execute a given task. Through imitation robots can learn from their environment and adapt to it just as human newborns do. Despite different facets of imitative behaviours observed in humans and higher primates, imitation in robotics has usually been implemented as a process of copying demonstrated actions onto the movement apparatus of the (...) robot. While the results being reached are impressive, we believe that a shift towards a higher expression of imitation, namely the comprehension of human actions and inference of its intentions, is needed. In order to be useful as human companions, robots must act for a purpose by achieving goals and fulfilling human expectations. In this paper we present ConSCIS, an architecture for goal-level imitation in robotics where the focus is put on final effects of actions on objects. The architecture tightly links low-level data with high-level knowledge, and integrates, in a unified framework, several aspects of imitation, such as perception, learning, knowledge representation, action generation and robot control. Some preliminary experimental results with an anthropomorphic arm/hand robotic system are shown. (shrink)

Imitation in robotics is seen as a powerful means to reduce the complexity of robot programming. It allows users to instruct robots by simply showing them how to execute a given task. Through imitation robots can learn from their environment and adapt to it just as human newborns do. Despite different facets of imitative behaviours observed in humans and higher primates, imitation in robotics has usually been implemented as a process of copying demonstrated actions onto the movement apparatus of the (...) robot. While the results being reached are impressive, we believe that a shift towards a higher expression of imitation, namely the comprehension of human actions and inference of its intentions, is needed. In order to be useful as human companions, robots must act for a purpose by achieving goals and fulfilling human expectations. In this paper we present ConSCIS, an architecture for goal-level imitation in robotics where the focus is put on final effects of actions on objects. The architecture tightly links low-level data with high-level knowledge, and integrates, in a unified framework, several aspects of imitation, such as perception, learning, knowledge representation, action generation and robot control. Some preliminary experimental results with an anthropomorphic arm/hand robotic system are shown. (shrink)

Conceptual space is proposed as an intermediate representation level between the psychological and the neurobiological levels of descriptions of appraisal and emotions. The main advantage of the proposed intermediate representation is that the appraisal and emotions dynamics are described by using the terms of geometry.

The conceptual space< (Gärdenfors 2000) is discussed as a representation structure that connects the constructivist and the ecological vision subsystems in an operating autonomous robot based on computer vision.

The experiments proposed in the article by Steels & Belpaeme (S&B) can be considered as a starting point toward a general methodology for the automatic learning of conceptual spaces.