An architecture for emotional and context-aware associative learning for robot companions

Caroline Rizzi Raymundo, Colin G. Johnson, Patricia A. Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This work proposes a theoretical architectural model based on the brain's fear learning system with the purpose of generating artificial fear conditioning at both stimuli and context abstraction levels in robot companions. The proposed architecture is inspired by the different brain regions involved in fear learning, here divided into four modules that work in an integrated and parallel manner: the sensory system, the amygdala system, the hippocampal system and the working memory. Each of these modules is based on a different approach and performs a different task in the process of learning and memorizing environmental cues to predict the occurrence of unpleasant situations. The main contribution of the model proposed here is the integration of fear learning and context awareness in order to fuse emotional and contextual artificial memories. The purpose is to provide robots with more believable social responses, leading to more natural interactions between humans and robots.

Original languageEnglish
Title of host publicationProceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication
PublisherIEEE
Pages31-36
Number of pages6
ISBN (Print)9781467367042
DOIs
Publication statusPublished - 20 Nov 2015
Event24th IEEE International Symposium on Robot and Human Interactive Communication 2015 - Kobe, Japan
Duration: 31 Aug 20154 Sep 2015

Conference

Conference24th IEEE International Symposium on Robot and Human Interactive Communication 2015
Abbreviated titleRO-MAN 2015
CountryJapan
CityKobe
Period31/08/154/09/15

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition

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