Seminar on Biologically Motivated Systems

ABSTRACT

Recently, the computer software society is becoming aware that the traditional programming solutions are increasing in complexity day by day. To solve a highly complex problem, a highly complex solution must be also developed. The chance of an error being generated is very high; and moreover complex programs tends to crash more often and if the code is changed to rectify the error, another error may emerge. The maintenance and upgrade of the code is very costly and can often cause a company a fortune. Therefore many fortune 500 companies are searching to more efficient solutions for economic and chronological reasons. They have recognized that solutions to problems encountered in real life in the modern society are not conceivable even by the intelligence of human beings. A higher intelligence is required. But us there a higher intelligence than human beings? Yes There is. Although no individual entity in a species is capable of displaying intelligence as high as humans, groups of animals often exhibit behavior that are highly intelligent than the individual mettle., and that too in orders of magnitude, when it comes to solving a particular problem. The intelligence emerging from such groups are known as Swarm Intelligence. Swarm Intelligence often exceed the human intelligence by many times. Therefore, it seems reasonable to leave the complex and huge tasks to such intelligence and have them solved. Setting simple rules and making the swarm elements interact with each other with result in the solution of the problem leads to the success of a biologically motivated system.

INTRODUCTION

Nature has been in the constant process of evolving better and better solutions for creating more and more intelligent and adaptive animals through various methods. These methods have attained perfection in the course of millions of years, which if they are replicated into computer systems, can result in very efficient algorithms. The use of examples from biology is a well trodden path to understanding the behavior of complex systems. The natural world is after all the global ecosystem in which we and all other organisms live out our lives and contains species which are either in competition or collaboration or symbiotic in their relationships. So it is natural for us to look for similarities between the way animal communities live and computer programs. We can use biological system as metaphors or analogies. If the metaphor seems to be working in pointing up similar features we test it as a metaphor for explaining the possible relationships that exist. Taken farther we may even build computer models which simulate to some degree actual behavior. But there is a limit. True complex systems are evolving systems and as such are neither predictable nor can be expected to repeat their history in any exact way. Groups of animals often exhibit behavior that are highly intelligent than the individual mettle. These behaviors are called group behavior. For example are swarming of bees, flocking of birds, herding of cattle, schooling of fish etc. The intelligence emerging from such groups are known as Swarm Intelligence. Swarm Intelligence often exceed the human intelligence by many times. Therefore, it seems reasonable to leave the complex and huge tasks to such intelligence and have them solved. But the tricky part is the incorporation of these algorithms into the computer. Setting simple rules and making the swarm elements interact with each other with result in the solution of the problem leads to the success of a biologically motivated system.