Autonomous Agent Using AI Q-Learning in Augmented Reality Ludo Board Game

An autonomous agent works with Artificial Intelligence (AI) can decide its actions to adapt and respond to the changes in a dynamic environment. The autonomous agent can be developed in games as a Non-Player Character (NPC) to interact with the changes of state in the game environment. Traditional board games such as Ludo have had many players since the olden days but slowly lost attraction to the public, especially the younger generations as digital games become more popular. Although the Ludo board game can be digitized to fascinate the players through implementing Augmented Reality (AR) technology in handheld devices, common NPCs found in games have determined actions and are unable to learn from experience and adapt to the changes of the game environment. Therefore, this research aims to develop an autonomous agent for board game in handheld AR (HAR). The first step in the three main phases is to examine the autonomous agent for the HAR board game. The second phase is developing the AR board game with Q-learning and Minimax algorithms for board game agents. Finally, the third phase is integrating the AR board game with Q-learning and Minimax agents in handheld. The novel contribution of this research is the redesign of Ludo for AR with autonomous agent and generate the training data using the Q-Learning algorithm to create autonomous agent in AR.