Emotions and Context in Decision-making for a Socially Intelligent Agent
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Date
2024-04-03
Authors
Malhotra, Aarti
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Artificial intelligence (AI) research has largely focused on rational thinking, decision
making, goal achievement, and reward maximization. Emotions have been considered nonessential,
or even detrimental, when designing and implementing AI systems. With the
advancement in affective computing research and the increasing adoption of AI agents
as part of human society, there is a growing need to have a deeper connection between
human and machine. Although significant efforts have been made in affective computing
towards recognizing human emotions and generating human-like emotions, there has been
less progress towards using emotions to guide decision and understanding human social
context. This thesis focuses on emotions and context in decision-making, towards building
socially intelligent agents, that are adaptive and emotionally aligned with humans.
I first conducted a systematic review of the literature on implemented systems for
decision making that used emotions. I synthesized extracted data into four conceptual
model types, viz. Matching, Appraisal and Coping, Decision-theoretic and Parametric
and provided a process view of each type. Then, I implemented one such model as a
brain-inspired neural model. The aim was to model the role of affect guiding decisionmaking,
resulting in interactions that are similar to human interactions, while inhibiting
some behaviors based on the social context. The model was implemented using Nengo, a
python library for building and simulating large-scale neural models, using spiking neurons.
I then investigated how to supply such a model with context, known to be a very important
factor in emotional-based decision making. I proposed a computer vision spatio-temporal
transformer model and its variations for joint learning and prediction, and evaluated on an
existing Video Group Affect dataset. Improvements to social event prediction were shown
by utilizing affective information. Finally, I considered a real-world care-giving scenario
which demonstrates the potential of our model for establishing an emotional relationship
and interaction between older adults, care partners, people living with dementia, and three
exemplar robots.
The insights gained in this thesis may encourage AI and affective computing research
to develop agents that can simulate human affective and decision-making mechanisms, and
in the process understand humans better.
Description
Keywords
Emotions, Context, Decision-making, Socially Intelligent Agent, Affective Computing, Artificial Intelligence, Brain-inspired