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      Intelligent architectures for robotics: The merging of cognition and emotion

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          Abstract

          What is the place of emotion in intelligent robots? In the past two decades, researchers have advocated for the inclusion of some emotion-related components in the general information processing architecture of autonomous agents, say, for better communication with humans, or to instill a sense of urgency to action. The framework advanced here goes beyond these approaches and proposes that emotion and motivation need to be integrated with all aspects of the architecture. Thus, cognitive-emotional integration is a key design principle. Emotion is not an "add on" that endows a robot with "feelings" (for instance, reporting or expressing its internal state). It allows the significance of percepts, plans, and actions to be an integral part of all its computations. It is hypothesized that a sophisticated artificial intelligence cannot be built from separate cognitive and emotional modules. A hypothetical test inspired by the Turing test, called the Dolores test, is proposed to test this assertion.

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          Most cited references34

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          Uncovering the overlapping community structure of complex networks in nature and society

          Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
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            Cortical high-density counterstream architectures.

            Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.
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              Action sets and decisions in the medial frontal cortex.

              Activations in human dorsomedial frontal and cingulate cortices are often present in neuroimaging studies of decision making and action selection. Interpretations have emphasized executive control, movement sequencing, error detection and conflict monitoring. Recently, however, experimental approaches, using lesions, inactivation, and cell recording, have suggested that these are just components of the areas' functions. Here we review these results and integrate them with those from neuroimaging. A medial superior frontal gyrus (SFG) region centred on the pre-supplementary motor area (pre-SMA) is involved in the selection of action sets whereas the anterior cingulate cortex (ACC) has a fundamental role in relating actions to their consequences, both positive reinforcement outcomes and errors, and in guiding decisions about which actions are worth making.
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                Author and article information

                Journal
                01 February 2019
                Article
                1902.00363
                8eadb9ac-9f56-4d9c-a07b-c170c3fa4f31

                http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                Custom metadata
                7 figures
                cs.RO cs.AI

                Robotics,Artificial intelligence
                Robotics, Artificial intelligence

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