Title :
Data mining considerations for knowledge acquisition in real time strategy games
Author :
Iuhasz, Gabriel ; Munteanu, Victor Ion ; Negru, Viorel
Author_Institution :
Dept. of Comput. Sci., West Univ. of Timisoara, Timisoara, Romania
Abstract :
Adaptive Game AI has been one of the key topics being researched in the field of academic game AI research. In this paper we present a comparison of several domain independent machine learning methods with the aid of which we extract expert knowledge from game logs. Each game log is represented as a feature vector that encodes cardinality and timing for player actions. We compare a wide variety of classification methods and highlight which ones are best for deployment for an adaptive game AI systems.
Keywords :
data mining; feature extraction; knowledge acquisition; learning (artificial intelligence); multi-agent systems; pattern classification; serious games (computing); academic game AI research; adaptive game AI systems; classification method; data mining; expert knowledge extraction; feature vector; game logs; independent machine learning methods; knowledge acquisition; player action cardinality; player action timing; real time strategy games; Data mining; Feature extraction; Games; Predictive models; Timing; Vectors; Artificial Intelligence; Cloud Computing; Machine Learning; Multi-Agent Systems; Video Games;
Conference_Titel :
Intelligent Systems and Informatics (SISY), 2013 IEEE 11th International Symposium on
Conference_Location :
Subotica
Print_ISBN :
978-1-4799-0303-0
DOI :
10.1109/SISY.2013.6662596
