Mind, Body, World- Foundations of Cognitive Science, 2013a

artificial neural network, the perceptron, has been offered as a viable alternative
to classical theory (Dawson et al., 2010). In this section we briefly describe a third
approach to the reorientation task, because embodied cognitive science has studied
it in the context of behaviour-based robotics.
Classical and connectionist cognitive science provide very different accounts
of the co-operative and competitive interactions between geometric and featural
cues when an agent attempts to relocate the target location in a reorientation
arena. However, these different accounts are both representational. One of the
themes pervading embodied cognitive science is a reaction against representational
explanations of intelligent behaviour (Shapiro, 2011). One field that has been
a test bed for abandoning internal representations is known as new wave robotics
(Sharkey, 1997).
New wave roboticists strive to replace representation with reaction (Brooks,
1999), to use sense-act cycles in the place of representational sense-think-act processing.
This is because “embodied and situated systems can solve rather complicated
tasks without requiring internal states or internal representations” (Nolfi & Floreano,
2000, p. 93). One skill that has been successfully demonstrated in new wave robotics
is navigation in the context of the reorientation task (Lund & Miglino, 1998).
The Khepera robot (Bellmore & Nemhauser, 1968; Boogaarts, 2007) is a standard
platform for the practice of new wave robotics. It has the appearance of a motorized
hockey puck, uses two motor-driven wheels to move about, and has eight sensors
distributed around its chassis that allow it to detect the proximity of obstacles.
Roboticists have the goal of combining the proximity detector signals to control motor
speed in order to produce desired dynamic behaviour. One approach to achieving
this goal is to employ evolutionary robotics (Nolfi & Floreano, 2000). Evolutionary
robotics involves using a genetic algorithm (Holland, 1992; Mitchell, 1996) to find a
set of weights between each proximity detector and each motor.
In general, evolutionary robotics proceeds as follows (Nolfi & Floreano, 2000).
First, a fitness function is defined, to evaluate the quality of robot performance.
Evolution begins with an initial population of different control systems, such as different
sets of sensor-to-motor weights. The fitness function is used to assess each
of these control systems, and those that produce higher fitness values “survive.”
Survivors are used to create the next generation of control systems via prescribed
methods of “mutation.” The whole process of evaluate-survive-mutate is iterated;
average fitness is expected to improve with each new generation. The evolutionary
process ends when improvements in fitness stabilize. When evolution stops, the
result is a control system that should be quite capable of performing the task that
was evaluated by the fitness function.
Lund and Miglino (1998) used this procedure to evolve a control system that
enabled Khepera robots to perform the reorientation task in a rectangular arena
240 Chapter 5