Poker is used to measure progresses in extensive-form games research due to its unique characteristics: it is a game where playing agents have to deal with incomplete information and stochastic scenarios and a large number of decision points. The development of Poker agents has seen significant advances in one-on-one matches but there are still no consistent results in multiplayer and in games against human experts. In order to allow for experts to aid the improvement of the agents’ performance, we have created a high-level strategy specification language. To support strategy definition, we have also developed an intuitive graphical tool. Additionally, we have also created a strategy inferring system, based on a dynamically weighted Euclidean distance. This approach was validated through the creation of simple agents and by successfully inferring strategies from 10 human players. The created agents were able to beat previously developed mid-level agents by a good profit margin.