在感知噪声下，考虑不确定性的决策策略显著提升生存率。
Risk-Aware Decision Policies for Agents Under Noisy Perception
- 设计生物启发的捕食-被捕食模型，引入噪声感知下的决策机制
- 不考虑不确定性的策略随噪声增加导致生存率骤降，而感知不确定性策略显著提升存活率
- 适合研究鲁棒决策、生物智能或含噪环境下的强化学习
生物系统的感知本质上存在噪声，导致个体在不确定性中做出决策，误判可能带来代价高昂甚至致命后果。本文构建了一个受噪声感知影响的仿生捕食-被捕食觅食模型，对比了不同决策策略在对感知预测进行不确定性考量时的表现。在对称与非对称感知噪声条件下，控制实验表明：盲目信任感知标签会导致噪声增大时出现灾难性失败，而考虑不确定性的策略显著提升生存率并减少致命错误。我们还观察到行为模式的定性转变——随着不确定性升高，代理从探索型策略转向保守型。该模型将风险敏感觅食、生态信息利用与人工生命相结合，证明显式信息获取可增强感知不可靠时的鲁棒性。结果强调了不确定性感知决策的重要性，并提供了一个可解释的人工生命范例，用于模拟带噪声标签的鲁棒学习。
原文摘要 · Abstract (English)
Perception in biological systems is inherently noisy, requiring organisms to make decisions under uncertainty where misclassification can be costly or fatal. We present an Artificial Life predator-prey model of foraging under noisy perception, and compare agent performance when using various policies that take into account their noisy predictions. Through controlled experiments under both symmetric and asymmetric perceptual noise, we show that blindly trusting perceptual labels leads to catastrophic failure as noise increases, while uncertainty-aware strategies significantly improve survival and reduce fatal errors. We further observe qualitative regime shifts in behaviour, with agents transitioning from exploratory to conservative strategies as uncertainty increases. Our model links risk-sensitive foraging, ecological information use, and Artificial Life by showing that explicit information gathering can improve robustness when perception is unreliable. These results highlight the importance of uncertainty-aware decision-making and provide an interpretable artificial life analogue to robust learning with noisy labels.
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