Computational Modeling, Autonomous Robots, & Embodied Cognition
Philosophy 321h, Bates College
Narrative:
This
course explores current research models in embodied cognition and artificial
intelligence that use simulations, robotics, and genetic algorithms to explore
the kids of flexible and adaptive behaviors constitutive of our concept of
intelligence. These approaches provide
alternative models for intelligent behavior that challenges traditional representational
and computational theories of mind. Standard computational theories of mind
model intelligent behavior on the human capacity for rational decision making
and general problem solving. On this account thoughts are constructed from
abstract symbols that represent aspects of the environment, and minds are
treated as general purpose symbol manipulation systems that can be realized in
any of a potentially infinite number natural organisms or artificial systems.
Although the computational model of mind has been a powerful research tool in
cognitive science, it has proven difficult to implement in computer simulations
and mobile robotics. For instance, general purpose problem solvers fall prey to
what is called the frame problem in artificial intelligence: they have
difficulty filtering task salient information out of noisy signals and so often
follow inefficient procedures or get stuck in blind alleys. This and other
difficulties have inspired researchers in embodied cognition and artificial
life to look towards insect and animal models of intelligent behavior for
alternatives.
Our bodies
have evolved in lock step with cognitive systems, and both can be thought of as
adaptations fine‐tuned to the kinds of environmental features necessary
to help satisfy an organisms’ basic needs and interests. As a result the
general structure of an organism's body (i.e., its effectors and the structure
and placement of its peripheral sensory organs) is a strong constraint on the
ways it acquires, manipulates, and uses information from its environment – a
constraint that focuses cognitive systems on task salient information in the
environment, simplifying the computational demands of flexible and adaptive
behavior. Researchers in embodied cognition and artificial life therefore
challenge the assumption that minds are general purpose symbol manipulation
systems . They argue instead that intelligent behavior emerges from the
interaction between (evolved and well adapted) bodies and the environment. To
this end research in autonomous (sometimes called agent‐ or behavior‐based)
robotics is used to explore the role of agent‐environment interactions in
the production of intelligent behavior.
Embodied
theories of cognition have proven to be equally powerful research tools.
However, one can question whether the kinds of adaptive behaviors that can be
successfully captured by insect and animal models have the structure necessary
to explain the full range of human cognitive behaviors that interest us (e.g.,
counterfactual reasoning processes involved in long term planning). At the very
least these types of processes seem to require abstract concepts that enable
organisms to consider alternative strategies, model potential environmental
change, and represent novel outcomes. This suggests that rather than think of
embodied and computational approaches as mutually exclusive alternatives,
perhaps it is better to argue that they compliment one another and can be used
together to produce a more comprehensive theory of cognition. The course explores
this dialectic. Readings are drawn from contemporary sources in philosophy,
psychology, neuroscience, and computer science. In addition to course readings
and written assignments, students use a range of computer simulations and
robotics exercises to explore the ideas introduced in class.
see Rolf Pfeifer's TEDX Talk, "How The Body Shapes the Way
We Think"