Evans, M.H., Fox, C.W. and Prescott, T.J. (2014) Machines learning - Towards a new synthetic autobiographical memory. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J. and Verschure, P.F.M.J., (eds.) Biomimetic and Biohybrid Systems. Third International Conference, Living Machines, July 30 – August 1, 2014, Milan, Italy. Lecture Notes in Computer Science, 8608 . Springer International Publishing , pp. 84-96. ISBN 9783319094342
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. © 2014 Springer International Publishing.
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: | © 2014 Springer International Publishing Switzerland. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Dec 2016 15:45 |
Last Modified: | 24 Mar 2018 17:02 |
Published Version: | https://doi.org/10.1007/978-3-319-09435-9_8 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-09435-9_8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107054 |