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.

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.

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)

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.

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 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.

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

I will present the rationale followed for the conceptualization and the following development the Dual PECCS system that relies on the cognitively grounded heterogeneous proxytypes representational hypothesis. Such hypothesis allows integrating exemplars and prototype theories of categorization and has provided useful insights in the context of cognitive modelling for what concerns the typicality effects in categorization. As argued in [Chella et al., 2017] [Lieto et al., 2018b] [Lieto et al., 2018a] a pivotal role in this respect is played by (...) the use of the conceptual spaces framework and by its integration with a symbolic knowledge representation layer. (shrink)

We define prenormal modal logics and show that S1, S1, S0.9, and S0.9 are Lewis versions of certain prenormal logics, determination and decidability for which are immediate. At the end we characterize Cresswell logics and ponder C. I. Lewis's idea of strict implication in S1.

A textbook on modal logic, intended for readers already acquainted with the elements of formal logic, containing nearly 500 exercises. Brian F. Chellas provides a systematic introduction to the principal ideas and results in contemporary treatments of modality, including theorems on completeness and decidability. Illustrative chapters focus on deontic logic and conditionality. Modality is a rapidly expanding branch of logic, and familiarity with the subject is now regarded as a necessary part of every philosopher's technical equipment. Chellas here offers an (...) up-to-date and reliable guide essential for the student. (shrink)

Recent theories of agency (sees to it that) of Nuel Belnap and Michael Perloff are examined, particularly in the context of an early proposal of the author.

Having gained some idea of what MacIntosh logics there are, we conclude this paper with a remark about the totality of them. Let theterritory of a rule or condition be the class of all modal logics that have the rule or satisfy the condition. What is MacIntosh territory, the class of all normal logics with the MacIntosh rule, like? What is its structure?

The publication of this book is an event in the making. All over the world scientists, psychologists, and philosophers are waiting to read Antonio Damasio's new theory of the nature of consciousness and the construction of the self. A renowned and revered scientist and clinician, Damasio has spent decades following amnesiacs down hospital corridors, waiting for comatose patients to awaken, and devising ingenious research using PET scans to piece together the great puzzle of consciousness. In his bestselling Descartes' Error, (...) Damasio revealed the critical importance of emotion in the making of reason. Building on this foundation, he now shows how consciousness is created. Consciousness is the feeling of what happens-our mind noticing the body's reaction to the world and responding to that experience. Without our bodies there can be no consciousness, which is at heart a mechanism for survival that engages body, emotion, and mind in the glorious spiral of human life. A hymn to the possibilities of human existence, a magnificent work of ingenious science, a gorgeously written book, The Feeling of What Happens is already being hailed as a classic. (shrink)

Este texto es el producto de un intercambio que parte del reconocimiento de una profunda crisis cultural de la Educacion e intenta, desde los bordes de los saberes institucionalizados localizar lugares compartidos de deflexión situada ...

L'uomo è l'essere aperto, mancante, indefinito, in quanto tale bisognoso al suo essere.

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)

In his paper Leblanc seeks to supplant traditional forms of semantic theory with truth-value analyses. I have tried, here, to extend the scope, if not the limits, of his results. But now, in closing, I wish to register some reservations about his notion of relevance.Leblanc eschews the customary semantic analysis of intensional languages — the so-called ‘possible worlds’ semantics — as making ‘metaphysical virtue out of logical necessity’. And so he would replace such accounts with ‘truth-value’ analyses. But, alas, these (...) theories are seen to have only limited application. Either all the atoms of a language must be evaluated by the functions in W in a truth-value ℋ 〈W, α, R〉, or the functions must be indexed. Because I share Leblanc's opinion that it ought to be that only the atoms of a theory are linguistically pertinent to it, I opt for the second alternative. Here, of course, things work out all right. But, as Leblanc notes, indexing the functions is equivalent to doing semantics in the usual way. In either case, new parameters are required for the evaluation of theories. So more than just the truth-values of a theory's atoms is relevant.My concern is with natural languages. It has long been recognized — though only more recently well articulated4 — that an adequate semantic analysis of such languages, with their indexical and intensional features, must relativize the notion of truth to various indices, viz., aspects of the contexts of utterance of sentences. It is clear that the truth-value of ‘If your feet hurt, then it may rain tomorrow’ depends on more than just the truth-values of its atoms: the speaker, hearer, place, and time of utterance of the sentence are at least to be included among the extra-linguistic features required for an evaluation of the sentence. Such indices thus provide various realizations of the notion of a possible world, and they are all relevant. (shrink)