Evans, MH, Fox, CW and Prescott, TJ (2014) Machines Learning - Towards a New Synthetic Autobiographical Memory. In: Duff, A, Lepora, NF, Mura, A, Prescott, TJ and Verschure, PFMJ, (eds.) Biomimetic and Biohybrid Systems. 3rd International Conference on Biomimetic and Biohybrid Systems: Living Machines 2014, 30 Jul - 01 Aug 2014, Milan, Italy. Lecture Notes in Computer Science, 8608 . Springer Verlag , pp. 84-96. ISBN 978-3-319-09434-2
Abstract
Autobiographical memory is the organisation of episodes and contextual information from an individual’s experiences into a coherent narrative, which is key to our sense of self. Formation and recall of autobiographical memories is essential for effective, adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions such as event reconstruction. A synthetic autobiographical memory system would endow intelligent robotic agents with many essential components of cognition through active compression and storage of historical sensorimotor data in an easily addressable manner. Current approaches neither fulfil these functional requirements, nor build upon recent understanding of predictive coding, deep learning, nor the neurobiology of memory. This position paper highlights desiderata for a modern implementation of synthetic autobiographical memory based on human episodic memory, and proposes that a recently developed model of hippocampal memory could be extended as a generalised model of autobiographical memory. Initial implementation will be targeted at social interaction, where current synthetic autobiographical memory systems have had success.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing Switzerland 2014. This is an author produced version of a paper published in Lecture Notes in Computer Science. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-09435-9_8. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Synthetic, Autobiographical, Memory, Episodic, Hippocampus, Robotics, Predictive, Coding, Deep, Learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jun 2016 09:28 |
Last Modified: | 16 Jan 2018 08:01 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-09435-9_8 |
Status: | Published |
Publisher: | Springer Verlag |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-319-09435-9_8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99260 |