Knowledge-Based General Game Playing
| dc.contributor.author | Haufe, Sebastian | |
| dc.contributor.author | Michulke, Daniel | |
| dc.contributor.author | Schiffel, Stephan | |
| dc.contributor.author | Thielscher, Michael | |
| dc.date.accessioned | 2018-01-08T09:14:53Z | |
| dc.date.available | 2018-01-08T09:14:53Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | Although we humans cannot compete with computers at simple brute-force search, this is often more than compensated for by our ability to discover structures in new games and to quickly learn how to perform highly selective, informed search. To attain the same level of intelligence, general game playing systems must be able to figure out, without human assistance, what a new game is really about. This makes General Game Playing in ideal testbed for human-level AI, because ultimate success can only be achieved if computers match our ability to master new games by acquiring and exploiting new knowledge. This article introduces five knowledge-based methods for General Game Playing. Each of these techniques contributes to the ongoing success of our FLUXPLAYER (Schiffel and Thielscher in Proceedings of the National Conference on Artificial Intelligence, pp. 1191–1196, 2007), which was among the top four players at each of the past AAAI competitions and in particular was crowned World Champion in 2006. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11189 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 25, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Fluxplayer | |
| dc.subject | General game playing | |
| dc.subject | Knowledge representation | |
| dc.title | Knowledge-Based General Game Playing | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 33 | |
| gi.citation.startPage | 25 |