TY - GEN
T1 - Using genetically optimized artificial intelligence to improve gameplaying fun for strategical games
AU - Salge, C.
AU - Lipski, C.
AU - Mathiak, B.
AU - Mahlmann, Tobias
N1 - Original paper can be found at : http://portal.acm.org/citation.cfm?id=1401843 Copyright ACM [Full text of this article is not available in the UHRA]
PY - 2008
Y1 - 2008
N2 - Fun in computer games depends on many factors. While some factors like uniqueness and humor can only be measured by human subjects, in a strategical game, the rule system is an important and measurable factor. Classics like chess and GO have a millennia-old story of success, based on clever rule design. They only have a few rules, are relatively easy to understand, but still they have myriads of possibilities. Testing the deepness of a rule-set is very hard, especially for a rule system as complex as in a classic strategic computer game. It is necessary, though, to ensure prolonged gaming fun. In our approach, we use artificial intelligence (AI) to simulate hours of beta-testing the given rules, tweaking the rules to provide more game-playing fun and deepness. To avoid making the AI a mirror of its programmer's gaming preferences, we not only evolved the AI with a genetic algorithm, but also used three fundamentally different AI paradigms to find boring loopholes, inefficient game mechanisms and, last but not least, complex erroneous behavior.
AB - Fun in computer games depends on many factors. While some factors like uniqueness and humor can only be measured by human subjects, in a strategical game, the rule system is an important and measurable factor. Classics like chess and GO have a millennia-old story of success, based on clever rule design. They only have a few rules, are relatively easy to understand, but still they have myriads of possibilities. Testing the deepness of a rule-set is very hard, especially for a rule system as complex as in a classic strategic computer game. It is necessary, though, to ensure prolonged gaming fun. In our approach, we use artificial intelligence (AI) to simulate hours of beta-testing the given rules, tweaking the rules to provide more game-playing fun and deepness. To avoid making the AI a mirror of its programmer's gaming preferences, we not only evolved the AI with a genetic algorithm, but also used three fundamentally different AI paradigms to find boring loopholes, inefficient game mechanisms and, last but not least, complex erroneous behavior.
U2 - 10.1145/1401843.1401845
DO - 10.1145/1401843.1401845
M3 - Conference contribution
SN - 978-1-60558-173-6
SP - 7
EP - 14
BT - Procs of the 2008 ACM SIGGRAPH symposium on Video games (Sandbox 2008)
PB - ACM Press
ER -