Study of learning algorithms in games
In many computer games various bots operate against the player. Unlike algorithms for static targets which already exist, these bots require algorithms to search for moving targets and these algorithms are inherently more computation and resource intensive. In some computer games, search algorith...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2009
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/18947 |
| _version_ | 1811691501556596736 |
|---|---|
| author | Rahul Bhasker |
| author2 | Loh Kok Keong, Peter |
| author_facet | Loh Kok Keong, Peter Rahul Bhasker |
| author_sort | Rahul Bhasker |
| collection | NTU |
| description | In many computer games various bots operate against the player. Unlike
algorithms for static targets which already exist, these bots require algorithms to
search for moving targets and these algorithms are inherently more computation
and resource intensive. In some computer games, search algorithms utilize as
much as 70% of CPU time and hence design requirements of these algorithms
necessitate good computation and execution efficiency.
The typical moving target search algorithm repeatedly applies the A* algorithm to
the moving target to find a partial solution and execute the move. However, as the
size of the problem space increases, the size of the heuristic table required grows
exponentially. In previous work, the Abstraction MTS significantly reduced the
memory requirements and showed improved performance over other existing MTS
algorithms by clustering the problem space into “abstract groups”. However, the
existing Abstraction MTS has no learning component and hence the performance is
constant even if the same problem space is used repeatedly. This project explores
the re-introduction of learning into the Abstraction MTS, without reducing the
performance of the basic Abstraction MTS algorithm. |
| first_indexed | 2024-10-01T06:20:54Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/18947 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:20:54Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/189472023-03-03T20:24:44Z Study of learning algorithms in games Rahul Bhasker Loh Kok Keong, Peter School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence In many computer games various bots operate against the player. Unlike algorithms for static targets which already exist, these bots require algorithms to search for moving targets and these algorithms are inherently more computation and resource intensive. In some computer games, search algorithms utilize as much as 70% of CPU time and hence design requirements of these algorithms necessitate good computation and execution efficiency. The typical moving target search algorithm repeatedly applies the A* algorithm to the moving target to find a partial solution and execute the move. However, as the size of the problem space increases, the size of the heuristic table required grows exponentially. In previous work, the Abstraction MTS significantly reduced the memory requirements and showed improved performance over other existing MTS algorithms by clustering the problem space into “abstract groups”. However, the existing Abstraction MTS has no learning component and hence the performance is constant even if the same problem space is used repeatedly. This project explores the re-introduction of learning into the Abstraction MTS, without reducing the performance of the basic Abstraction MTS algorithm. Bachelor of Engineering (Computer Engineering) 2009-08-25T09:10:50Z 2009-08-25T09:10:50Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/18947 en Nanyang Technological University 64 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Rahul Bhasker Study of learning algorithms in games |
| title | Study of learning algorithms in games |
| title_full | Study of learning algorithms in games |
| title_fullStr | Study of learning algorithms in games |
| title_full_unstemmed | Study of learning algorithms in games |
| title_short | Study of learning algorithms in games |
| title_sort | study of learning algorithms in games |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | http://hdl.handle.net/10356/18947 |
| work_keys_str_mv | AT rahulbhasker studyoflearningalgorithmsingames |