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

eorganizing the entire system. The scalability and flexibility of a swarm make it
robust, as it can continue to compute when some of its component agents no longer
function properly. Notice how these advantages of a swarm of agents are analogous
to the advantages of connectionist networks over classical models, as discussed in
Chapter 4.
Nonlinearity is also a key ingredient of swarm intelligence. For a swarm to be
considered intelligent, the whole must be greater than the sum of its parts. This
idea has been used to identify the presence of swarm intelligence by relating the
amount of work done by a collective to the number of agents in the collection
(Beni & Wang, 1991). If the relationship between work accomplished and number
of agents is linear, then the swarm is not considered to be intelligent. However, if
the relationship is nonlinear—for instance, exponentially increasing—then swarm
intelligence is present. The nonlinear relationship between work and numbers may
itself be mediated by other nonlinear relationships. For example, Dawson, Dupuis,
and Wilson (2010) found that in collections of simple LEGO robots, the presence
of additional robots influenced robot paths in an arena in such a way that a sorting
task was accomplished far more efficiently.
While early studies of robot collectives concerned small groups of homogenous
robots (Gerkey & Mataric, 2004), researchers are now more interested in complex
collectives consisting of different types of machines for performing diverse tasks
at varying locations or times (Balch & Parker, 2002; Schultz & Parker, 2002). This
leads to the problem of coordinating the varying actions of diverse collective members
(Gerkey & Mataric, 2002, 2004; Mataric, 1998). One general approach to solving
this coordination problem is intentional co-operation (Balch & Parker, 2002;
Parker, 1998, 2001), which uses direct communication amongst robots to prevent
unnecessary duplication (or competition) between robot actions. However, intentional
co-operation comes with its own set of problems. For instance, communication
between robots is costly, particularly as more robots are added to a communicating
team (Kube & Zhang, 1994). As well, as communication makes the functions
carried out by individual team members more specialized, the robustness of the
robot collective is jeopardized (Kube & Bonabeau, 2000). Is it possible for a robot
collective to coordinate its component activities, and solve interesting problems, in
the absence of direction communication?
The embodied approach has generated a plausible answer to this question via
stigmergy (Kube & Bonabeau, 2000). Kube and Bonabeau (2000) demonstrated that
the actions of a large collective of robots could be stigmergically coordinated so that
the collective could push a box to a goal location in an arena. Robots used a variety
of sensors to detect (and avoid) other robots, locate the box, and locate the goal
location. A subsumption architecture was employed to instantiate a fairly simple
set of sense-act reflexes. For instance, if a robot detected that is was in contact with
Elements of Embodied Cognitive Science 233