Biologically-inspired episodic memory model considering the context information

Gyeong Moon Park, Sanghyun Cho, Jong Hwan Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Episodic memory can store time sequential events and retrieve them anytime with specific cues. However, if the episodic memory only stores events comprised of actions and objects, execution of episodes may fail if current situation is different from the settings it learned in. As a solution, we propose Deep C-ART (Context-Adaptive Resonance Theory) which considers not only time sequential events but also their contexts. In addition to the learning process of Deep ART, Deep C-ART stores context information such as situation of objects, states of robots, place, and time of episodes. Since context changes over each event in an episode, Deep C-ART forms an episode with an event sequence and a context sequence. During retrieval and execution of episode, it compares the current situation with the learned one to verify that it is executable or in an anomaly situation. The effectiveness of Deep C-ART is demonstrated through computer simulations.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1471-1476
Number of pages6
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 6 Feb 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country/TerritoryHungary
CityBudapest
Period9/10/1612/10/16

Keywords

  • Context
  • Deep C-ART
  • Episodic memory
  • Situated object

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