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Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent's reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent's private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning. [ABSTRACT FROM AUTHOR]