Interactive Learning Neural Networks for Predicting Game Behavior

Authors:
Qiaomei Sun;Guang Ren;Xiaowei Qi
Affiliations:
Marine Engineering College, Dalian Maritime University, Dalian, China 116026;Marine Engineering College, Dalian Maritime University, Dalian, China 116026;Marine Engineering College, Dalian Maritime University, Dalian, China 116026
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

Game theory is an interdisciplinary approach to the study of human behavior. Games describe a widely accepted framework for representing interactive decision-making. Artificial Neural Networks (ANNs) are universal approximators and have the ability of learning. Combining ANNs with game representation, we introduced a new architecture by which the learning abilities of ANNs are utilized to predict game behavior. Based on previous work, we investigated further the potential value of neural networks for modeling and predicting human interactive learning in repeated games. We conducted simulation studies based on the new model using experiments data which are provided by authors other than this paper. Through computer simulations and comparing with other models, we demonstrated that our model is superior in many respects to other models on ten experiments.