Uncertainty Handling in Surrogate Assisted Optimisation of Games Dissertation Abstract

摘要

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.