A survey from 17 human testers shows that the levels with our collaborative opponents are more difficult but interesting than those with either the original Pac-Man's personalities or the non-cooperative greedy opponents. In this paper, we introduce a cooperative strategy based on the A* algorithm for enemies' AI in the Pac-Man game. Since game users interact with more than one opponent in most of today's games, collaboration control of opponent characters becomes more important than ever before. Smart opponents can make games more challenging as well as allow players for diverse experiences, even in the same game environment. The artificial intelligence (AI) of Non-Player Companions (NPC), especially opponents, is a key element to adjust the level of games in game design. It progresses from highly achievable paths to the less achievable ones, thus configuring and personalizing the rehabilitation process. The results from two samples show the effectiveness of the Monte-Carlo Tree Search in the RehabGame by being able to build a coherent hand motion. It monitors the enactment of the muscles strain and stress through the Myo armband sensor and provides the next step required for the rehabilitation purpose. Limb movements are stored in the search tree which is used to determine the location of new target virtual fruit objects by accessing the data saved in the background from different game plays. The game collects orientation, muscle and joint activity data and utilizes them to make decisions on game progression. The system has the capability of adjusting the difficulty level to the player's ability by learning from the movements made and generating further subsequent objects. Through the process of a real-time rehabilitation game, the player decides on paths that could be taken by her/his upper limb in order to reach virtual goal objects. The objective of the game is to combat the physical impairment of stroke/ brain injury casualties in order to improve upper limb movement. In this paper, we present a rehabilitation system known as RehabGame in which the Monte-Carlo Tree Search algorithm is used. Development of artificial intelligence techniques in real time decision-making games can provide a platform for the examination of tree search algorithms.
Artificial and Computational Intelligence in computer games play an important role that could simulate various aspects of real life problems.
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