@inproceedings{c0b10f1c93a34878b045705915c3c522,
title = "Multi-objective adaptation of a parameterized GVGAI agent towards several games",
abstract = "This paper proposes a benchmark for multi-objective optimization based on video game playing. The challenge is to optimize an agent to perform well on several different games, where each objective score corresponds to the performance on a different game. The benchmark is inspired from the quest for general intelligence in the form of general game playing, and builds on the General Video Game AI (GVGAI) framework. As it is based on game-playing, this benchmark incorporates salient aspects of game-playing problems such as discontinuous feedback and a non-trivial amount of stochasticity. We argue that the proposed benchmark thus provides a different challenge from many other benchmarks for multi-objective optimization algorithms currently available. We also provide initial results on categorizing the space offered by this benchmark and applying a standard multi-objective optimization algorithm to it.",
keywords = "GVGAI, MCTS, Multi-objective optimization",
author = "Ahmed Khalifa and Mike Preuss and Julian Togelius",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 ; Conference date: 19-03-2017 Through 22-03-2017",
year = "2017",
doi = "10.1007/978-3-319-54157-0_25",
language = "English (US)",
isbn = "9783319541563",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "359--374",
editor = "Oliver Sch{\"u}tze and Gunter Rudolph and Kathrin Klamroth and Yaochu Jin and Heike Trautmann and Christian Grimme and Margaret Wiecek",
booktitle = "Evolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings",
}