2The utility of this model is that tanks could potentially be made to move across a
terrain taking into account only tanks that are close by. A similar use of the model
might be the self-organization of a squadron of flying drones.
2.2.EMERGENT PROBLEM SOLVING
2.2.1.OVERVIEW
Emergent problem solving is a characteristic of swarm systems. Emergent problem
solving is a class of problem solving where the behavior of individual agents is not
goal directed; i.e. by looking at the behavior of single agents little or no information
on the problem being solved can be inferred.
2.2.2 SWARM PROBLEM SOLVING
Swarm problem solving is a bottom-up approach to controlling and optimizing
distributed systems. It is a mindset rather than a technology that is inspired by the
behavior of social insects that has evolved over millions of years.
Peterson suggests that swarms calculate faster and organize better. Swarm systems
are characterized by simple agents interacting through the environment using signals
that are spatially (and temporally) distributed. By simple we mean that the agents
possess limited cognition and memory; sometimes no memory at all. Furthermore, the
behavior of individual agents is characterized by a small number of rules. In this
document we consider the complexity (or simplicity) of an agent to be a function of
the number of rules that are required to explain its behavior.
2.2.3.ADVANTAGES AND DISADVANTAGES
There are several advantages:
A. Agents are not goal directed; they react rather than plan extensively.
B. Agents are simple, with minimal behavior and memory.
C. Control is decentralized; there is no global information in the system.
D. Failure of individual agents is tolerated; emergent behavior is robust with respect
to individual failure.
E. Agents can react to dynamically changing environments.
F. Direct agent interaction is not required