Collective intelligence evolution using ant colony optimization and neural networks

Abstract

Recently, theory of collective intelligence (CI) evolution is proposed as a meta algorithm toward artificial general intelligence. But the only implementation of the CI algorithm of the theory is the Monte Carlo tree search (MCTS) used by AlphaZero. Since ant colony optimization (ACO) is an extensively used CI algorithm, it is useful to implement CI evolution using ACO. A genetic version of ACO is adapted to satisfy the CI evolution theory by two methods. One method is realized by using a policy network, namely policy network guided ACO (P-ACO). The other method is realized by using a policy network and a value network, namely policy and value network guided ACO (PV-ACO). Both methods of ACO evolution algorithm are applied to Tic-Tac-Toe and Four in a Row, where traditional ACO played poorly compared to the tree search algorithm, e.g., MCTS. Computational experiments are done to compare both methods with pure ACO and MCTS. As a result, the intelligence level of ACO evolution algorithm quickly exceeds pure ACO and MCTS. In this article, the performance of ACO evolution algorithm is analyzed and the feasibility of applying the CI evolution theory to a specific application is verified.