A game process is a system where the decisions of one agent can influence the decisions of other agents. In the real world, social influences and relationships between agents may influence the decision makings of agents with game behaviors. And in turn, this also gives us the possibility to mine some information from such agents , such as the relationships between them, by the interactions in a game process. In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches. We apply GGN to the team game as a concrete application and conduct experiments on relationship inference tasks .
A game process is a system where the decisions of one agent can influence the decisions of other agents. In the real world, social influences and relationships between agents may influence the decision makings of agents with game behaviors. And in turn, this also gives us the opportunity to mine such information from agents by the observed interactions of them in a game process. In this paper, we propose a Game Generative Network (GGN) framework that utilizes the deviation between the real game outcome and the ideal game model to generate networks for game processes, which opens a door for understanding agents with game behaviors by network mining approaches. We illustrate how to apply GGNs to infer the hidden relationships between agents with game behaviors and conduct experiments on team games as a concrete example. Experimental results demonstrate that our proposed framework can reveal the hidden relationships of agents in such games .
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possibility to mine some information from such agents , such as the relationships between them, by the interactions
opportunity to mine such information from agents by the observed interactions of them
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which
that
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build
generate
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more about
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graph
network
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apply GGN to the team game as a concrete application
illustrate how to apply GGNs to infer the hidden relationships between agents with game behaviors
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relationship inference tasks
team games as a concrete example. Experimental results demonstrate that our proposed framework can reveal the hidden relationships of agents in such games