17 research outputs found

    DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

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    This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

    On-Line Processing of Grammatical Structure Using Reservoir Computing

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    International audiencePrevious words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. The current research provides a possible explanation of how certain aspects of this on-line language processing can occur, based on the dynamics of recurrent cortical networks. We simulate prefrontal area BA47 as a recurrent network that receives on-line input of "grammatical" words during sentence processing, with plastic connections between cortex and striatum (homology with Reservoir Computing). The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different "semantic words" play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. This demonstrates that a RNN can decode grammatical structure from sentences in an on-line manner in order to generate a predictive representation of the meaning of the sentences

    Multiple levels of structure in language and music

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    Item does not contain fulltextA forum devoted to the relationship between music and language begins with an implicit assumption: There is at least one common principle that is central to all human musical systems and all languages, but that is not characteristic of (most) other domains. Why else should these two categories be paired together for analysis? We propose that one candidate for a common principle is their structure. In this chapter, we explore the nature of that structure—and its consequences for psychological and neurological processing mechanisms—within and across these two domain
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