08-01-2013, 12:04 PM
An application of the genetic programming technique to strategy development
An application of the genetic.pptx (Size: 1.86 MB / Downloads: 46)
Abstract:
In this paper, we will apply co-evolution and genetic programming (GP) techniques to develop two strategies: the ghost’s and players’ strategies in the Traffic Light Game.
(a popular game among children).
These two strategies compete against each other.
The development of these strategies has achieved phenomenal success.
The results encourage us to develop more complex strategies
such as
human learning models,
cooperative robotic models.
Introduction
In the past decades, human beings tried to develop computer intelligence such that the computer would possess powerful abilities in both computation and inference.
However, it is very difficult for a computer to automatically generate effective strategies.
GP is one kind of artificial intelligence technique which applies a genetic algorithm to programming.
initially, a set of computer programs are randomly generated. These then breed and give birth to a growing population using the Darwinian principle of fitness.
GP for strategy development:
Strategies:
In the Traffic Light Game research, we expected that the ghost and players would be able to develop some good strategies through evolution.
The ghost needs to evolve a strategy for detouring around these obstacles to chase the nearest ‘‘green light” player until all players call ‘‘red light” or a ‘‘green light” player is touched by the ghost
(see Fig. 2).
Conclusions:
In this research, we apply the GP technique to co-evolve the strategies of the ghost (attacker) and players (survivors) in the Traffic Light Game.
Based on the Darwinian Theory, the offspring will satisfy the predefined conditions to a greater and greater extent during strategy development.
Based on the bottom-up approach, the defined functions and actions combine automatically, dynamically, and objectively to form a good (optimal) strategy for solving a particular problem