Name
Abstract | ||
|---|---|---|
The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to fix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a realtime strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents. Game developers can benefit from applying ESTG, as it considerably reduces the time and effort needed to create adaptive game AI. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1609/aimag.v27i3.1894 | AI MAGAZINE |
Field | DocType | Volume |
Win-win game,Game mechanics,Computer science,Simulation,Game design,Simulations and games in economics education,Artificial intelligence,Game Developer,Sequential game,Non-cooperative game,Game development tool | Journal | 27 |
Issue | ISSN | Citations |
3 | 0738-4602 | 21 |
PageRank | References | Authors |
1.14 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc J. V. Ponsen | 1 | 57 | 5.47 |
| Hector Muñoz-Avila | 2 | 522 | 44.02 |
| Pieter Spronck | 3 | 475 | 51.04 |
| David W. Aha | 4 | 4103 | 620.93 |