Home // International Journal On Advances in Intelligent Systems, volume 14, numbers 1 and 2, 2021 // View article

Playing Halma with Swarm Intelligence

Authors:
Isabel Kuehner
Adrian Stock

Keywords: Swarm Intelligence; Traveling Salesman Problem; Ant Colony Optimization; Bee Colony Optimization; Halma

Abstract:
Swarm Intelligence algorithms are inspired by animals living together in swarms. Those algorithms are applicable to solve optimization problems like the Travelling Salesman Problem. Furthermore, they can be extended for playing games, e.g., board games. This paper proposes a novel approach for playing the board game Halma by combining Swarm Intelligence algorithms. It focuses on the implementation of a Swarm Intelligence player for the Halma game by combining two state-of-the-art algorithms, namely the Ant Colony Optimization, and the Bee Colony Optimization. In addition, we propose a modular Model View Controller software architecture for implementing the game and its players. Moreover, this paper evaluates the performance of the Swarm Intelligence agent for the single player and two player cooperative version of Halma. The algorithm presented in this paper is successful in learning the dynamics of the game and provides a stable basis for further research in this area.

Pages: 46 to 60

Copyright: Copyright (c) to authors, 2021. Used with permission.

Publication date: December 31, 2021

Published in: journal

ISSN: 1942-2679