opponent modeling has benefited a controlled agent's decision-making by
constructing models of other agents. Existing methods commonly assume access to
opponents' observations and actions, which is infeasible when opponents'
behaviors are unobservable or hard to obtain. We propose a no
本研究提出了一种 for identifying the priorities of the opponent in multi-issue negotiation from partial dialogues，通过关键词识别和数据适应的方法，对话模型在零散输入数据的情况下，能准确预测对手的所重视的议题顺序。