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...

Full description

Saved in:
Bibliographic Details
Main Authors: WANG, Di, TAN, Ah-hwee, MIAO, Chunyan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Online Access: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
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7280
record_format dspace
spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Cognitive model
Computational autobiographical memory model
Memory storage and retrieval
Wander in reminiscence
Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle 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
description 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.
format text
author WANG, Di
TAN, Ah-hwee
MIAO, Chunyan
author_facet 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
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url 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
_version_ 1770575928021221376