Intelligent Versus Random Beavers - an Agent-Based Approach in Facing the Busy Beaver Problem

Since Tibor Rado in 1962 defined the busy beaver game, several approaches have used computer technology to search for 'best' candidates to solve it. In this paper we follow an 'evolutionary approach' to solving it using agent-based techniques. This approach includes techniques to reduce the number of inspected Turing machines and to accelerate simulation of Turing machines using agent-based techniques; in particular we use the 'Swarm simulation toolkit'. Our approach uses a variety of learning techniques such as genetic algorithms, classifier systems multiple genetic algorithms and random search to explore the universe of the 'best solution' to the game