Swarm Intelligence Systems and Application
Swarm intelligence is the emergent collective intelligence of groups of simple autonomous agents. Here, an autonomous agent is a subsystem that interacts with its environment, which probably consists of other agents, but acts relatively independently from all other agents. The autonomous agent does not follow commands from a leader, or some global plan.
For example, for a bird to participate in a flock, it only adjusts its movements to coordinate with the movements of its flock mates, typically its neighbours that are close to it in the flock. A bird in a flock simply tries to stay close to its neighbours, but avoid collisions with them. Each bird does not take commands from any leader bird since there is no lead bird. Any bird can fly in the front, center or back of the swarm. Swarm behavior helps birds take advantage of several things including protection from predators (especially for birds in the middle of the flock), and searching for food (as each bird is essentially exploiting the eyes of every other bird).
During the course of the last 20 years, researchers have discovered the variety of interesting insect and animal behaviors in nature. A flock of birds sweeps across the sky. A group of ants forages for food. A school of fish swims, turns, flees together etc. We call this kind of aggregate motion Swarm behavior. Recently, biologists and computer scientists have studied how to model biological swarms to understand how such social animals interact, achieve goals, and evolve.
Furthermore, engineers are increasingly interested in this kind of swarm behavior since the resulting swarm intelligence can be applied in optimization (e.g. in telecommunication systems),robotics track patterns in transportation systems, and military applications. A high-level view of a swarm suggests that the N agents in the swarm are cooperating to achieve some purposeful behavior and achieve some goal. This apparent collective intelligence seems to emerge from what are often large groups of relatively simple agents. The agents use simple local rules to govern their actions and via the interactions of the entire group, the swarm achieves its objectives. A type of self organization emerges from the collection of actions of the group.
Principles Of Swarm Intelligence
OVERVIEW The objective of this engagement is to provide a comprehensive assessment of the state of the art in Swarm Intelligence; specifically the role of stigmergy in distributed problem solving. In order to do this, working definitions have to be provided along with the essential properties of systems that are swarm-capable; i.e. problem solving is an emergent property of a system of simple agents. The principle of stigmergy implies the interaction of simple agents through a common medium with no central control. This principle implies that querying individual agents tells one little or nothing about the emergent properties of the system.
Consequently, simulation is often used to understand the emergent dynamics of stigmergic systems. Stigmergic systems are typically stochastic in nature; individual actions being chosen probabilistic-ally from a limited behavioral repertoire. Actions performed by individual agents change the nature of the environment; for example a volatile chemical called a pheromone is deposited. This chemical signal is sensed by other agents and results in modified probabilistic choice of future actions.
The advantages of such a system are clear. Being a system in which multiple actions of agents are required for a solution to emerge, the activity of an individual agent is not as important. That is, stigmergic systems are resilient to the failure of individual agents and, more importantly still react extremely well to dynamically changing environments. Optimal use of resources is often a significant consideration in designing algorithms. Another stigmergic system, the raid army ant model efficiently and effectively forages for food using pheromone-based signalling.
In a raid army ant system, agents develop a foraging front that covers a wide path, leading to extremely effective food finding. This model has military value in that it could potentially be exploited as a series of mechanisms for searching for land mines, a problem that, tragically, is all too common in parts of the world. A third stigmergic model of military interest is that of flocking or aggregation. Here, large numbers of simple agents can be made to move through a space filled with obstacles (and potentially threats) without recourse to central control. The environmental signals here are the position and velocities of the agents themselves. The 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.
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.
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 .