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Wines with geographical indication can be classified and represented by such features as designations of origin, producers, vintage years, alcoholic strength, and grape varieties; these features allow us to define wines in terms of a set of necessary and/or sufficient conditions. However, wines can also be identified by other characteristics, involving their look, smell, and taste; in this case, it is hard to define wines in terms of necessary and/or sufficient conditions, as wine concepts exhibit typicality effects. This is a setback for the design of computer science ontologies aiming to represent wine concepts, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. To solve this problem, we propose to adopt a hybrid approach in which ontology-oriented formalisms are combined with a geometric representation of knowledge based on conceptual spaces. As in conceptual spaces, concepts are identified in terms of a number of quality dimensions. In order to determine those relevant for wine representation, we use the terminology developed by the Italian Association of Sommeliers to describe wines. This will allow us to understand typicality effects about wines, determine prototypes and better exemplars, and measure the degree of similarity between different wines.

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 [Laird (2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O'Reilly and Munakata (2000)]) are based on a purely connectionist model, while others (e.g. CLARION [Sun (2006)] 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 [Kurup and Chandrasekaran (2007)]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G\"ardenfors [G\"ardenfors (2000)] 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 [G\"ardenfors (1997)] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one., Comment: 31 pages, 3 figures in Biologically Inspired Cognitive Architectures, 2017

As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field (such as, in the first place, the Web Ontology Language – OWL) do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces.

Il problema dell’interfaccia (interface problem) consiste nel rendere conto delle relazioni che sussistono tra le nozioni psicologiche del discorso quotidiano e i resoconti scientifici del mentale. E’ probabile infatti che, nello sviluppo di una visione scientifica, naturalistica dell’uomo, saremo costretti ad abbandonare molte nozioni psicologiche ordinarie, che potrebbero risultare inadeguate rispetto agli scopi e ai metodi dell’indagine naturalistica. Tuttavia, la costruzione di modelli simulative di (parti di) un sistema cognitivo sotto la forma di artefatti e la verifica delle loro prestazioni attraverso una forma di test di Turing, potrebbe, almeno in linea di principio, consentirci di "reimmergere" le spiegazioni naturalistiche della cognizione nel mondo della nostra esperienza ordinaria, al fine di verificarne la corrispondenza con le nozioni del discorso mentale quotidiano e con le intuizioni del senso comune.

The project of neuroaesthetics could be interpreted as an attempt to identify a “neural essence” of art, i.e., a set of necessary and sufficient conditions formulated in the language of neuroscience, which define the concept art . Some proposals developed within this field can be read in this way. I shall argue that such attempts do not succeed in individuating a neural definition of art. Of course, the fact that the proposals available for defining art in neural terms do not work does not mean that such an enterprise is in principle doomed to failure. However, I maintain that there are good reasons to suspect that in general such a definition cannot be worked out. This does not mean, though, that the study of neural correlates in artwork production and fruition is a senseless project. Neuroaesthetics could succeed in individuating widespread mechanisms common to different forms of art coming from remote cultural contexts, which presumably rely on aspects of our mind and/or brain's functioning that are in...

Objective: One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot. Methods and material: We refer to a robot cognitive architecture that has been developed during almost 10 years at the RoboticsLab of the University of Palermo. The architecture is organized in three computational areas. The subconceptual area is concerned with the low level processing of perceptual data coming from the sensors. In the linguistic area, representation and processing are based on a logic formalism. In the conceptual area, the data coming from the subconceptual area are organized in conceptual categories. Results: To model higher order perceptions in self-reflective agents, we introduce the notion of second-order points in conceptual space. Each point in this space corresponds to a self-reflective agent, i.e., the robot itself, persons, and other robots with introspective capabilities. Conclusions: The described model of robot self-consciousness, although effective, highlights open problems from the point of view of the computational requirements of the current state-of-art computer systems. Some future works that lets the robot to summarize its own past experiences should be investigated.

In this work we present a hybrid knowledge representation system aiming at extending the representational and reasoning capabilities of classical ontologies by taking into account the theories of typicality in conceptual processing. The system adopts a categorization process inspired to the dual process theories and, from a representational perspective, is equipped with a heterogeneous knowledge base that couples conceptual spaces and ontological formalisms. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves the representational and reasoning “conceptual” capabilities of standard ontology-based systems.

In this work we present an ontology-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the representational and reasoning capabilities of classical ontological-based systems towards more realistic and cognitively grounded scenarios, such as those envisioned by the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality-based one (grounded on the conceptual spaces framework). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science and the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves on the representational and reasoning "conceptual" capabilities of standard ontology-based systems.

In the study of cognitive processes, limitations on computational resources (computing time and memory space) are usually considered to be beyond the scope of a theory of competence, and to be exclusively relevant to the study of performance. Starting from considerations derived from the theory of computational complexity, in this paper I argue that there are good reasons for claiming that some aspects of resource limitations pertain to the domain of a theory of competence.

A framework for high-level representations in computer vision architectures is described. The framework is based on the notion of conceptual space. This approach allows us to define a conceptual semantics for the symbolic representations of the vision system. In this way, the semantics of the symbols can be grounded to the data coming from the sensors. In addition, the proposed approach generalizes the most popular frameworks adopted in computer vision.

We propose a framework for the representation of visual knowledge in a robotic agent, with special attention to the understanding of dynamic scenes. According to our approach, understanding involves the generation of a high level, declarative description of the perceived world. Developing such a description requires both bottom-up, data driven processes that associate symbolic knowledge representation structures with the data coming out of a vision system, and top-down processes in which high level, symbolic information is in its turn employed to drive and further refine the interpretation of a scene. On the one hand, the computer vision community approached this problem in terms of 2D/3D shape reconstruction and of estimation of motion parameters. On the other, the AI community developed rich and expressive systems for the description of processes, events, actions and, in general, of dynamic situations. Nevertheless, these two approaches evolved separately and concentrated on different kinds of problems. We propose an architecture that integrates these two traditions in a principled way. Our assumption is that a link is missing between the two classes of representations mentioned above. In order to fill this gap, we adopt the notion of conceptual space (CS—Gardenfors (2000)), a representation where information is characterized in terms of a metric space. A CS acts as an intermediate representation between subconceptual (i.e., not yet conceptually categorized) information, and symbolically organized knowledge. The concepts of process and action have immediate characterizations in terms of structures in the conceptual space. The architecture is illustrated with reference to an experimental setup based on a vision system operating in a scenario with moving and interacting people.

Concept representation is still an open problem in the field of ontology engineering and, more in general, of knowledge representation. In particular, it still remains unsolved the problem of representing "non classical" concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions. In this chapter we review empirical evidence from cognitive psychology, which suggests that concept representation is not an unitary phenomenon. In particular, it seems that human beings employ both prototype and exemplar based representations in order to represent non classical concepts. We suggest that a similar, hybrid prototype-exemplar based approach could be useful also in the field of formal ontology technology.

This article introduces a cognitive model and an experimental system for the representation and real‐time processing of music and multimedia, based on both artificial intelligence (AI) and traditional techniques. First, it introduces some basic issues on the requirements of Al‐based music systems and the class of problems faced by our system (music and multimedia composition, real‐time performance, and analysis). Then, our proposal of a representation scheme — on the basis of an implemented system, called HARP — is described. It is based on the integration of different formalisms, able to manage the different nature and levels of music and multimedia objects, from the symbolic, knowledge level, dealing with multilevel, abstract representations and reasoning mechanisms, to the subsymbolic level, dealing with low‐level processes, images, and physical signals. In our model, the symbolic level integrates a semantic network language of the family of KL‐ONE with a temporal logic language and production...

The problem of concept representation is relevant for knowledge engineering and for ontology-based technologies. However, the notion of concept itself turns out to be highly disputed and problematic in cognitive science. In our opinion, one of the causes of this state of affairs is that the notion of concept is in some sense heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as, for example, compositionality on the one side and the need of representing prototypical information on the other. AI research in some way shows traces of this situation. In this paper we propose an analysis of this state of affairs, and we sketch some proposals for concept representation in formal ontologies which take advantage from suggestions coming from cognitive science and psychological research. In particular we take into account the distinction between prototype and exemplar accounts in explaining prototypical effects.

The problem of concept representation is relevant for many subfields of cognitive research, including psychology, philosophy and artificial intelligence. In particular, in recent years, it received great attention within knowledge representation, because of its relevance for knowledge engineering and for ontology-based technologies. However, the notion of concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs is that the notion of concept is in some sense heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as, for example, compositionality on the one side and the need of representing prototypical information on the other. AI research in some way shows traces of this situation. In this paper we propose an analysis of this state of affairs and sketch some proposal for concept representation in formal ontologies, which takes into account suggestions coming from psychological research. Our basic assumption is that knowledge representation technologies designed considering evidences coming from experimental psychology (and, therefore, more similar to the humans way of reasoning and organizing information) can have better results in real life applications (e.g. in the field of Semantic Web).

Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines features of both symbolic and analogical approaches, is based on the construction of analogical models of the reference for the internal representations, as introduced by Johnson-Laird. In this work, we propose a similar approach to the problem of knowledge representation and reasoning about actions and plans. We propose a hybrid approach, symbolic and analogical, in which the inferences are partially devolved to measurements on analogical models generated starting from the symbolic representation. the interaction between the symbolic and the analogical level is due to the fact that procedures are connected to some symbols, allowing generating, updating, and verifying the mental model. the hybrid model utilizes, for the symbolic component, a representation system based on the distinction between terminological and assertional knowledge. the terminological component adopts a SI-Net formalism, extended by temporal primitives. the assertional component is a subset of first-order logics. the analogical representation is a set of concurrent procedures modeling parts of the world, action processes, simulations, and metaphors based on force fields concepts. A particular case study, regarding the problem of the assembly of a complex object from parts, is taken as an experimental paradigm. © 1993 John Wiley Sons, Inc.

This paper describes the artificial intelligence (AI) methodologies and the system's architecture of a prototypical intelligent composer's assistant, called HARP (Hybrid Action Representation and Planning). The model is hybrid, since it stands on both symbolic (structured multiple inheritance semantic nets, SI-Nets) and analogic knowledge representation paradigms. Composition processes are modeled in this framework as plans to be accomplished to reach given goals. The paper focusses on the symbolic part of the system: how to store the analogic descriptions of processes in a knowledge base, and how to use them in a music composition environment.

In this article we question the utility of the distinction between conceptual and nonconceptual content in cognitive science, and in particular, in the empirical study of visual perception. First, we individuate some difficulties in characterizing the notion of "concept" itself both in the philosophy of mind and cognitive science. Then we stress the heterogeneous nature of the notion of nonconceptual content and outline the complex and ambiguous relations that exist between the conceptual/nonconceptual duality and other pairs of notions, such as top---down/bottom-up and modular/nonmodular. Finally we look in greater detail at the proposal developed by Jacob and Jeannerod (Ways of seeing. The scopes and limits of visual cognition. Oxford, UK: Oxford University Press, 2003), who apply the notion of nonconceptual content to empirical research on visual perception. After reconstructing their point of view on concepts, we try to reject their major arguments in support of the conceptual/nonconceptual distinction, i.e. the compositionality of thought and the fineness of grain of percepts.