Abstract
To combine neural learning with the sequential detection of hierarchies of sensory features, and to facilitate planning and script execution, we propose Request Confirmation Networks (ReCoNs). ReCoNs are spreading activation networks with units that contain an activation and a state, and are connected by typed directed links that indicate partonomic relations and spatial or temporal succession. By passing activation along the links, ReCoNs can perform both neural computations and controlled script execution. We demonstrate the application of ReCoNs in the context of performing simple arithmetic, based on camera images of mathematical expressions.
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Acknowledgements
The implementation and further development of MicroPsi and the MESH editor would not be possible without the contributions of Ronnie Vuine, Dominik Welland and Priska Herger. The implementation presented here is based on a thesis by Gallagher (2018). We are grateful for generous support by and discussions with Dietrich Dörner, Martin Nowak and the Epstein Foundation. Current work on MicroPsi is supported by the Program of Evolutionary Dynamics at Harvard University, Humanity Plus and MicroPsi Industries GmbH, Berlin.
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Bach, J., Gallagher, K. (2018). Request Confirmation Networks in MicroPsi 2. In: Iklé, M., Franz, A., Rzepka, R., Goertzel, B. (eds) Artificial General Intelligence. AGI 2018. Lecture Notes in Computer Science(), vol 10999. Springer, Cham. https://doi.org/10.1007/978-3-319-97676-1_2
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DOI: https://doi.org/10.1007/978-3-319-97676-1_2
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