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An Overview of the Distributed Integrated Cognition Affect and Reflection DIARC Architecture

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Cognitive Architectures

Abstract

DIARC has been under development for over 15 years. Different from other cognitive architectures like SOAR or ACT-R, DIARC is an intrinsically component-based distributed architecture scheme that can be instantiated in many different ways. Moreover, DIARC has several distinguishing features, such as affect processing and deep natural language integration, is open-world and multi-agent enabled, and allows for “one-shot instruction-based learning” of new percepts, actions, concepts, rules, and norms. In this chapter, we will present an overview of the DIARC architecture and compare it to classical cognitive architectures. After laying out the theoretical foundations, we specifically focus on the action, vision, and natural language subsystems. We then give two examples of DIARC configurations for “one-shot learning” and “component-sharing”. We also briefly mention different use cases of DIARC, in particular, for autonomous robots in human-robot interaction experiments and for building cognitive models.

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Notes

  1. 1.

    A detailed conceptual and empirical comparison of robotic infrastructures up to 2006 can be found in [35].

  2. 2.

    The description of additional relevant components, such as the Belief and Inference subsystem, the (Motion and Task) Planning subsystem, and the interfaces with other middleware, will have to await a different publication outlet.

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Acknowledgements

The work on DIARC has been supported by various research grants from the US National Science Foundation and the US Office of Naval Research over the years, most recently by NSF grant IIS1316809 and ONR grant N00014-16-1-2278.

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Scheutz, M., Williams, T., Krause, E., Oosterveld, B., Sarathy, V., Frasca, T. (2019). An Overview of the Distributed Integrated Cognition Affect and Reflection DIARC Architecture. In: Aldinhas Ferreira, M., Silva Sequeira, J., Ventura, R. (eds) Cognitive Architectures. Intelligent Systems, Control and Automation: Science and Engineering, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-97550-4_11

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