Marcello Frixione

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Th is paper describes a general f ramework for the formal izat ion of monoton ic reasoning about belief in a mul t iagent env i ronment . The agents* beliefs are modeled as logical theories. The reasoning about their beliefs is formalized in s t i l l another theory, which we call the theory of the computer . The f ramework is used to model non-omniscient(More)
A new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory(More)
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(More)
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(More)
Perceptual anchoring is the problem of creating and maintaining in time the connection between symbols and sensor data that refer to the same physical objects. This is one of the facets of the general problem of integrating symbolic and non-symbolic processes in an intelligent system. Gärdenfors’ conceptual spaces provide a geometric treatment of knowledge(More)
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(More)
We approach the integration between symbolic and subsymbolic processing within a hybrid model of visual perception, intended for an autonomous intelligent system. No hypotheses are made about the adequacy of this model as a model of human vision: the proposed model is currently under development for a robot system. We propose an associative mapping(More)
Traditional artificial intelligence studies generally approach the problem of representing knowledge following the so-called knowledge representation hypothesis, as formulated by Brian Smith. More recently the development of the connectionist paradigm has questioned the symbolic approach to the study of the mind bringing about a more articulated view of the(More)
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(More)
An architecture for autonomous agents is proposed, that integrates the functional and the behavioral approaches to robotics. The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing. The proposed architecture is(More)