Quantifying Engagement of Battle City with Different AI Strength

This paper explores the game sophistication of a classic video game called BattleCity. We choose two research target, one is the original version without AI was published by Namco in 1985 year, another one is establish by Python language and enemy use Breadth-First-Search (BFS) and Depth-First-Search (DFS) method to against human player. We compare these two version BattleCity to make clear whether the AI strength can effect human entertainment feeling, therefore we choose game refinement theory to analyze them. Game refinement theory is a new game theory which concerns about the interesting aspects of games using a game sophistication measure that is derived from a game progress model; it can judge the game quality reasonable and reliable. Due to the human playing and computer simulations for the two simple version of BattleCity games are conducted to collect the data while game refinement measure is employed for the assessment. The results indicate several interesting observations. Also in the future, game designer can use this achievement to consider the AI strength and entertainment balance.