Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89670
Title: Modeling autobiographical memory in human-like autonomous agents
Authors: Wang, Di
Tan, Ah-Hwee
Miao, Chunyan
Keywords: DRNTU::Engineering::Computer science and engineering
Cognitive Model
Computational Autobiographical Memory Model
Issue Date: 2016
Source: 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.
Abstract: 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.
URI: https://hdl.handle.net/10356/89670
http://hdl.handle.net/10220/47105
URL: https://dl.acm.org/citation.cfm?id=2937048
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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