Name
Abstract | ||
|---|---|---|
Automatically analysing games is an important challenge for automated game design, general game playing, and co-creative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack of a priori design knowledge and heuristics. In this paper we formally define hyperstate space graphs, a compressed form of state space graphs which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games which closely relate to the heuristics designed by hand for search-based AI agents; we show how hyperstate space graphs also relate to modern ideas about game design; and we point towards future applications for hyperstates across game AI research. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CIG.2019.8848026 | 2019 IEEE Conference on Games (CoG) |
Keywords | Field | DocType |
automated game design,general game playing,game analysis | Game analysis,Graph,Design knowledge,Computer science,A priori and a posteriori,Game design,Theoretical computer science,Heuristics,General game playing,State space | Conference |
ISSN | ISBN | Citations |
2325-4270 | 978-1-7281-1885-7 | 2 |
PageRank | References | Authors |
0.42 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Cook | 1 | 2 | 0.42 |
| Azalea Raad | 2 | 7 | 1.51 |