建置一個應用於戰略遊戲中戰術模擬的人工智慧

Building an Artificial Intelligence of Strategy Simulation for War Game

曾彥翔(Yen-Hsiang Tseng)

指導教授 ： 陳瑞發

淡江大學/工學院/資訊工程學系碩士班/碩士(2010年)

https://doi.org/10.6846/TKU.2010.00783

摘要

近年來，數位遊戲軟體蓬勃發展，大部份數位遊戲軟體都具有一些人工智慧的應用。由於遊戲人工智慧開發困難且複雜度高，長久以來應用於遊戲的人工智慧多較簡易難度有限，但是無法自我更新、成長的人工智慧只能被侷限在某種程度，使得遊戲開發者總是不斷增加難度或敵人數量來滿足玩家的需求。
本論文研究建置一個應用於戰略遊戲的人工智慧，以類神經網路為基礎計算戰場中的影響範圍，利用學習回饋的特性訓練電腦分析戰場上的影響範圍，使電腦在戰略遊戲中進行戰術模擬，根據分析後的結果去指揮士兵進行遊戲，讓電腦指揮官有類似人類玩家的學習行為，如此一來遊戲內容更具挑戰性。

關鍵字

策略模擬 ； 戰爭遊戲 ； 類神經網路 ； 人工智慧

並列摘要

In recent years, digital game software grows flourish, most of digital games software has designed artificial intelligence in game. Because the development of game artificial intelligence is difficult and complex, it is limited the growth of the game artificial intelligence. Most of artificial intelligence of digital game is without self-learning, so the game developers always increase the number of enemies to make the game difficult to be completed in short time.
In this thesis, we proposed an artificial intelligence of strategy simulation for war game. We use artificial neural networks to construct influence map, and train the computer to analysis the influence map. According to the result of a battle game, the system will feedback to the game artificial intelligence and learn the behavior of player. In this way, the game would become more challenging for user to play.

並列關鍵字

Strategy Simulation Game ； War Game ； Artificial Neural Networks ； Artificial Intelligence

參考文獻

學傳播研究所碩士論文。

Tansui, Taiwan, 2004.

[Bourg 04] David M. Bourg and Glenn Seemann, ”AI for Game Developers”,

O'Reilly Media, July 2004.

[Chao 06] Hsiao-Chuan Chao, ”A reinforcement self-learning model