Uncertainty Handling in Surrogate Assisted Optimisation of Games
| dc.contributor.author | Volz, Vanessa | |
| dc.date.accessioned | 2021-04-23T09:30:29Z | |
| dc.date.available | 2021-04-23T09:30:29Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Real-world problems are often affected by uncertainties of different types and from multiple sources. Algorithms created for expensive optimisation, such as model-based optimisers, introduce additional errors. We argue that these uncertainties should be accounted for during the optimisation process. We thus introduce a benchmark as well as a new surrogate-assisted evolutionary algorithm to investigate this hypothesis further. The benchmark includes two function suites based on procedural content generation for games, which is a common problem observed in games research and also mirrors several types of uncertainties in the real-world. We find that observing and handling the uncertainty present in the problem can improve the optimiser, and also provides valuable insight into the function characteristics. | de |
| dc.identifier.doi | 10.1007/s13218-019-00613-1 | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-019-00613-1 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36280 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 34, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Procedural Content Generation | |
| dc.subject | Surrogate-assisted Evolutionary Algorithms | |
| dc.subject | Uncertainty handling | |
| dc.title | Uncertainty Handling in Surrogate Assisted Optimisation of Games | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 99 | |
| gi.citation.startPage | 95 |