Emotions, Motivation-Based Action Selection and Dynamic Environments
Abstract
In contrast to traditional approaches, where the focus is on developing or
evolving artificial “brains” as the route to artificial intelligence (AI) more
recent approaches have increasingly emphasised and modelled the role of
“bodies” and “environments”. In turn, this has further encouraged ideas
regarding aspects of intelligence as being best thought of as distributed
across agent brains, bodies and environments. That is, as system properties
emerging from interactions of these components. Action selection
is commonly recognised as one of the problems all agents, whether biological
or artificial, must face: deciding at any given moment “what to do
next”. Researchers have generated many different action selection mechanisms
as “solutions” to this problem. However, in the work of this thesis,
we focus on one which takes its inspiration from biological ideas about
the role and possible neural substrates of emotion. We use this to consider
how models of brain-body-environment interactions might be more
useful for the study of emotion, as well as action selection mechanisms.
For, despite the many mechanisms proposed, the literature still lacks systematic
ways to analyse their performance in combination with different
physical and/or perceptual capabilities. That is, factors relating more directly
to agent embodiment. In this thesis we have studied the performance
of our selected architecture in a robotic predator-prey scenario known as
the Hazardous Three Resource Problem. The predator-prey relationship
is popular in artificial intelligence, both as an action selection problem
and a situation which enables study of agent-agent interactions. Predators
can act as catalysts for the evolution of prey agents in a “survival of the
fittest” sense while, in their turn, prey agents are tests of predator ingenuity.
For us, however, it is also a situation where emotion might naturally
be assumed to have useful functions. To study action selection, emotion
and brain-body-environment interactions in an artificial predator-prey relationship,
we both advocate and adopt a bottom-up, animat approach. The
animat approach to AI is one that emphasizes characteristics neglected by
more traditional approaches. As such, it has embraced the study of robotic
agents. One reason for this is the process of designing “real-world” agents
forces us to consider practicalities simulations might not. What makes the
use of robots particularly appealing for our work, however, is how it can
give us a greater appreciation of more physical aspects of intelligence such
as agent morphology and its integration with agent control mechanisms as
well as environmental dynamics. Using LEGO robots, we show how the
performance of our architecture varies in our chosen scenario with aspects
of agent brain, body and environment. We argue our results complement
existing research by contributing evidence from a real-world implementation,
explicitly modelling ideas about action selection and emotion as
distributed across, or best thought of as emerging from interactions between,
agent brain, body and environment. In particular, this thesis shows
how our selected architecture varies and benefits from further integration
with aspects of agent “body”. It also acts as an example of an alternative
form for the bottom-up development of artificial emotion, demonstrating
wider applications for creating more adaptive action selection mechanisms.
Comparing the robotic predator-prey relationships we have created
to ethological evidence and theories, we argue our architecture may also
have specific potential for future research and applications — having already
proven itself capable of emerging multiple functions and properties
Publication date
2013-12-19Published version
https://doi.org/10.18745/th.23942https://doi.org/10.18745/th.23942
Funding
Default funderDefault project
Other links
http://hdl.handle.net/2299/23942Metadata
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