Two approaches for bidding in games are presented: knowledge-based approach and simulation-based approach. A general knowledge-based decision model for bidding in games with its strategy encoded in a Bayesian network was designed. A program for playing four-player tarok was implemented incorporating a specialised instance of the decision model and a simulation module for bidding. Both approaches were compared. The knowledge-based decision model was further compared to human experts, showing that it performs on par with them.
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