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-smu-ink.sis_research-72802021-11-23T08:03:18Z Modeling autobiographical memory in human-like autonomous agents WANG, Di TAN, Ah-hwee MIAO, Chunyan 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 bottomup 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. 2016-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6277 https://ink.library.smu.edu.sg/context/sis_research/article/7280/viewcontent/Modeling_Autobiographical_Memory_in_Human_Like_Autonomous_Agents___AAMAS_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Cognitive model Computational autobiographical memory model Memory storage and retrieval Wander in reminiscence Artificial Intelligence and Robotics Databases and Information Systems |
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Cognitive model Computational autobiographical memory model Memory storage and retrieval Wander in reminiscence Artificial Intelligence and Robotics Databases and Information Systems 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 bottomup 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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WANG, Di TAN, Ah-hwee MIAO, Chunyan |
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WANG, Di TAN, Ah-hwee MIAO, Chunyan |
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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 |
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Modeling autobiographical memory in human-like autonomous agents |
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Modeling autobiographical memory in human-like autonomous agents |
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modeling autobiographical memory in human-like autonomous agents |
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Institutional Knowledge at Singapore Management University |
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2016 |
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https://ink.library.smu.edu.sg/sis_research/6277 https://ink.library.smu.edu.sg/context/sis_research/article/7280/viewcontent/Modeling_Autobiographical_Memory_in_Human_Like_Autonomous_Agents___AAMAS_2016.pdf |
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