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

fYear

2013

fDate

26-28 Sept. 2013

Firstpage

331

Lastpage

336

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Informatics (SISY), 2013 IEEE 11th International Symposium on

Conference_Location

Subotica

Print_ISBN

978-1-4799-0303-0

Type

conf

DOI

10.1109/SISY.2013.6662596

Filename

6662596