Corpus ID: 17931578

Predicting Success in an Imperfect-Information Game

@inproceedings{Bakkes2007PredictingSI,
  title={Predicting Success in an Imperfect-Information Game},
  author={S. Bakkes and P. Spronck and J. Herik and Philip Kerbusch},
  year={2007}
}

S. Bakkes, P. Spronck, +1 author Philip Kerbusch

Published 2007

One of the most challenging tasks when creating an adaptation mechanism is to transform domain knowledge into an evaluation function that adequately measures the quality of the generated solutions. The high complexity of modern video games makes the task to generate a suitable evaluation function for adaptive game AI even more difficult. Still, our aim is to fully automatically generate an evaluation function for adaptive game AI. This paper describes our approach, and discusses the experiments… CONTINUE READING

ticc.uvt.nl

Save to Library

Create Alert

Cite

Launch Research Feed