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

obot has stopped moving, and two are light sensors that are used to attract the
robot to locations of bright illumination. The sense-act reflexes of antiSLAM were
not evolved but were instead created using the subsumption architecture.
The lowest level of processing in antiSLAM is “drive,” which essentially uses the
outputs of the ultrasonic sensors to control motor speed. The closer to an obstacle a
sensor gets, the slower is the speed of the one motor that the sensor helps to control.
The next level is “escape.” When both rotation sensors are signaling that the robot is
stationary (i.e., stopped by an obstacle detected by both sensors), the robot executes
a turn to point itself in a different direction. The next level up is “wall following”:
motor speed is manipulated in such a way that the robot has a strong bias to keep
closer to a wall on the right than to a wall on the left. The highest level is “feature,”
which uses two light sensors to contribute to motor speed in such a way that it
approaches areas of brighter light.
AntiSLAM performs complex, lifelike exploratory behaviour when placed
in general environments. It follows walls, steers itself around obstacles, explores
regions of brighter light, and turns around and escapes when it finds itself stopped
in a corner or in front of a large obstacle.
When placed in a reorientation task arena, antiSLAM generates behaviours
that give it the illusion of representing geometric and feature cues (Dawson,
Dupuis, & Wilson, 2010). It follows walls in a rectangular arena, slowing to a halt
when enters a corner. It then initiates a turning routine to exit the corner and
continue exploring. Its light sensors permit it to reliably find a target location
that is associated with particular geometric and local features. When local features
are removed, it navigates the arena using geometric cues only, and it produces
rotational errors. When local features are moved (i.e., an incorrect corner
is illuminated), its choice of locations from a variety of starting points mimics the
same combination of geometric and feature cues demonstrated in experiments
with animals. In short, it produces some of the key features of the reorientation
task—however, it does so without creating a cognitive map, and even without
representing a goal. Furthermore, observations of antiSLAM’s reorientation task
behaviour indicated that a crucial behavioural measure, the path taken by an agent
as it moves through the arena, is critical. Such paths are rarely reported in studies
of reorientation.
The reorienting robots discussed above are fairly recent descendants of Grey
Walter’s (1963) Tortoises, but their more ancient ancestors are the eighteenthcentury
life-mimicking, clockwork automata (Wood, 2002). These devices brought
into sharp focus the philosophical issues concerning the comparison of man and
machine that was central to Cartesian philosophy (Grenville, 2001; Wood, 2002).
Religious tensions concerning the mechanistic nature of man, and the spiritual
nature of clockwork automata, were soothed by dualism: automata and animals
Elements of Embodied Cognitive Science 243