Modeling autobiographical memory in human-like autonomous agents
Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories...
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sg-ntu-dr.10356-896702019-12-10T14:56:50Z Modeling autobiographical memory in human-like autonomous agents Wang, Di Tan, Ah-Hwee Miao, Chunyan School of Computer Science and Engineering Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016) NTU-UBC Research Centre of Excellence in Active Living for the Elderly DRNTU::Engineering::Computer science and engineering Cognitive Model Computational Autobiographical Memory Model Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one's life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory model established by the psychologists, which has been supported by neural imaging evidence. Specifically, the bottom-up memory search and the top-down memory readout operations of AM-ART replicate how the brain encodes and retrieves autobiographical memories. Furthermore, the wandering in reminiscence function of AM-ART mimics how human wanders in mind. For evaluations, we conducted experiments on a data set collected from the public domain to test the performance of AM-ART in response to exact, partial, and noisy memory retrieval cues. Moreover, our statistical analysis shows that AM-ART can simulate the phenomenon of wandering in reminiscence. NRF (Natl Research Foundation, S’pore) Published version 2018-12-19T07:29:55Z 2019-12-06T17:30:49Z 2018-12-19T07:29:55Z 2019-12-06T17:30:49Z 2016-05-01 2016 Conference Paper Wang, D., Tan, A.-H., Miao, C. (2016). Modelling autobiographical memory in human-like autonomous agents. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016), 845-853. https://hdl.handle.net/10356/89670 http://hdl.handle.net/10220/47105 https://dl.acm.org/citation.cfm?id=2937048 193903 en © 2016 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). This paper was published in Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016) and is made available as an electronic reprint (preprint) with permission of International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). The published version is available at: [https://dl.acm.org/citation.cfm?id=2937048]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 9 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering Cognitive Model Computational Autobiographical Memory Model Wang, Di Tan, Ah-Hwee Miao, Chunyan Modeling autobiographical memory in human-like autonomous agents |
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Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one's life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory model established by the psychologists, which has been supported by neural imaging evidence. Specifically, the bottom-up memory search and the top-down memory readout operations of AM-ART replicate how the brain encodes and retrieves autobiographical memories. Furthermore, the wandering in reminiscence function of AM-ART mimics how human wanders in mind. For evaluations, we conducted experiments on a data set collected from the public domain to test the performance of AM-ART in response to exact, partial, and noisy memory retrieval cues. Moreover, our statistical analysis shows that AM-ART can simulate the phenomenon of wandering in reminiscence. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Wang, Di Tan, Ah-Hwee Miao, Chunyan |
format |
Conference or Workshop Item |
author |
Wang, Di Tan, Ah-Hwee Miao, Chunyan |
author_sort |
Wang, Di |
title |
Modeling autobiographical memory in human-like autonomous agents |
title_short |
Modeling autobiographical memory in human-like autonomous agents |
title_full |
Modeling autobiographical memory in human-like autonomous agents |
title_fullStr |
Modeling autobiographical memory in human-like autonomous agents |
title_full_unstemmed |
Modeling autobiographical memory in human-like autonomous agents |
title_sort |
modeling autobiographical memory in human-like autonomous agents |
publishDate |
2018 |
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https://hdl.handle.net/10356/89670 http://hdl.handle.net/10220/47105 https://dl.acm.org/citation.cfm?id=2937048 |
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1681048127156518912 |