Vicarious Value Learning and Inference in Human-Human and Human-Robot Interaction
Lowe, Robert ; Almér, Alexander ; Gander, Pierre LU and Balkenius, Christian LU
(2019)
First International Workshop Social Emotions
p.395-400
- Abstract
- Among the biggest challenges for researchers of human-robot interaction is imbuing robots with lifelong learning capacities that allow efficient interactions between humans and robots. In order to address this challenge we are developing computational mechanisms for a humanoid robotic agent utilizing both system 1 and system 2-like cognitive processing capabilities. At the core of this processing is a Social Affective Appraisal model that allows for vicarious value learning and inference. Using a multi-dimensional reinforcement learning approach the robotic agent learns affective value-based functions (system 1). This learning can ground representations of affective relations (predicates) relevant to interacting agents. In this article we... (More)
- Among the biggest challenges for researchers of human-robot interaction is imbuing robots with lifelong learning capacities that allow efficient interactions between humans and robots. In order to address this challenge we are developing computational mechanisms for a humanoid robotic agent utilizing both system 1 and system 2-like cognitive processing capabilities. At the core of this processing is a Social Affective Appraisal model that allows for vicarious value learning and inference. Using a multi-dimensional reinforcement learning approach the robotic agent learns affective value-based functions (system 1). This learning can ground representations of affective relations (predicates) relevant to interacting agents. In this article we discuss the existing theoretical basis for developing our neural network model as a system 1-like process. We also discuss initial ideas for developing system 2-like top-down/generative affective (semantic relation-based) processing. The aim of the symbolic-connectionist architectural development is to promote autonomous capabilities in humanoid robots for interacting efficiently/intelligently (recombinant application of learned associations) with humans in changing and challenging environments. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d9a37c8b-cc4a-4dba-88fd-1b197a70fd65
- author
- Lowe, Robert
; Almér, Alexander
; Gander, Pierre
LU
and Balkenius, Christian
LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- First International Workshop Social Emotions
- conference location
- Cambridge, United Kingdom
- conference dates
- 2019-09-03 - 2019-09-03
- external identifiers
-
- scopus:85077814279
- ISBN
- 978-1-7281-3891-6
- 978-1-7281-3892-3
- DOI
- 10.1109/ACIIW.2019.8925235
- language
- English
- LU publication?
- yes
- id
- d9a37c8b-cc4a-4dba-88fd-1b197a70fd65
- date added to LUP
- 2019-09-05 14:16:45
- date last changed
- 2024-10-02 11:57:26
@inproceedings{d9a37c8b-cc4a-4dba-88fd-1b197a70fd65,
abstract = {{Among the biggest challenges for researchers of human-robot interaction is imbuing robots with lifelong learning capacities that allow efficient interactions between humans and robots. In order to address this challenge we are developing computational mechanisms for a humanoid robotic agent utilizing both system 1 and system 2-like cognitive processing capabilities. At the core of this processing is a Social Affective Appraisal model that allows for vicarious value learning and inference. Using a multi-dimensional reinforcement learning approach the robotic agent learns affective value-based functions (system 1). This learning can ground representations of affective relations (predicates) relevant to interacting agents. In this article we discuss the existing theoretical basis for developing our neural network model as a system 1-like process. We also discuss initial ideas for developing system 2-like top-down/generative affective (semantic relation-based) processing. The aim of the symbolic-connectionist architectural development is to promote autonomous capabilities in humanoid robots for interacting efficiently/intelligently (recombinant application of learned associations) with humans in changing and challenging environments.}},
author = {{Lowe, Robert and Almér, Alexander and Gander, Pierre and Balkenius, Christian}},
booktitle = {{2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)}},
isbn = {{978-1-7281-3891-6}},
language = {{eng}},
pages = {{395--400}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Vicarious Value Learning and Inference in Human-Human and Human-Robot Interaction}},
url = {{http://dx.doi.org/10.1109/ACIIW.2019.8925235}},
doi = {{10.1109/ACIIW.2019.8925235}},
year = {{2019}},
}