IEEE Trans Cybern. 2020 Jan;50(1):211-221. doi: 10.1109/TCYB.2018.2868405. Epub 2018 Sep 18.
This paper presents a systematic and interpretable design approach to generate type-2 (T2) fuzzy logic-based linguistic pursuing strategies (PSs) and their deployment to a real-world pursuit-evasion game (PEG). First, we have developed a novel T2 fuzzy logic-based strategy planner (T2-FSP). Then, through detailed theoretical investigations on the input-output mapping of the T2-FSP, it has been shown that it is possible to design a linguistic PS which defines both pursuer's approaching behavior (aggressive, smooth) and side (left or right) to the evader by simply tuning the footprint of uncertainty (FOU) sizes of the T2 fuzzy sets. Hence, an interpretable relationship has been revealed between the FOU sizes and the PSs through comparative theoretical explorations and derivations. Additionally, as there is a need to employ different PSs in a dynamic PEG environment, a type-1 fuzzy decision making (T1-FDM) mechanism has been designed to tune the FOU sizes of the T2-FSP and, thus, adjust the PS to be employed in real time. A real-world game environment is constructed in order to validate the developed T2 fuzzy logic-based PSs and T1-FDM mechanism in real time. Comparative experimental results have been presented to show that the T2 fuzzy logic-based PSs have satisfactory performance against a human user.
本文提出了一种系统的和可解释的设计方法，用于生成基于类型 2（T2）模糊逻辑的语言追求策略（PS），并将其部署到真实的追求-逃避游戏（PEG）中。首先，我们开发了一种新颖的基于 T2 模糊逻辑的策略规划器（T2-FSP）。然后，通过对 T2-FSP 的输入-输出映射进行详细的理论研究，表明通过简单地调整 T2 模糊集的不确定性足迹（FOU）大小，可以设计一种语言 PS，该 PS 定义了追逐者对逃避者的接近行为（激进、平滑）和侧面（左或右）。因此，通过比较理论探索和推导，揭示了 FOU 大小与 PS 之间的可解释关系。此外，由于需要在动态 PEG 环境中使用不同的 PS，因此设计了一种基于类型 1 模糊决策（T1-FDM）的机制来调整 T2-FSP 的 FOU 大小，从而实时调整要使用的 PS。构建了一个真实的游戏环境，以实时验证所开发的基于 T2 模糊逻辑的 PS 和 T1-FDM 机制。提出了比较实验结果，以表明基于 T2 模糊逻辑的 PS 对人类用户具有令人满意的性能。
