Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2016

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 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.

Keywords

Cognitive model, Computational autobiographical memory model, Memory storage and retrieval, Wander in reminiscence

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016, Singapore

First Page

845

Last Page

853

ISBN

9781450342391

Publisher

IFAAMAS

City or Country

Richland, SC

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