Name
Title | ||
|---|---|---|
A Hierarchical Reinforcement Learning Based Artificial Intelligence for Non-Player Characters in Video Games. |
Abstract | ||
|---|---|---|
Nowadays, video games conforms a huge industry that is always developing new technology. In particular, artificial intelligence techniques have been used broadly in the well-known non-player characters (NPC) given the opportunity to users to feel video games more real. This paper proposes the usage of the MaxQ-Q hierarchical reinforcement learning algorithm in non-player characters in order to increase the experience of the user in terms of naturalness. A case study of an NPC with the proposed artificial intelligence based algorithm in a first personal shooter video game was developed. Experimental results show that this implementation improves naturalness from the user's point of view. In addition, the proposed MaxQ-Q based algorithm in NPCs allow to programmers a robust way to give artificial intelligence to them. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13650-9_16 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Hierarchical reinforcement learning,non-player characters,naturalness,human assessment,video games | Computer science,Naturalness,Artificial intelligence,Reinforcement learning algorithm,Machine learning,Learning classifier system,Reinforcement learning | Conference |
Volume | ISSN | Citations |
8857 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiram E. Ponce | 1 | 26 | 13.63 |
| Ricardo Padilla | 2 | 0 | 0.68 |