感知噪声下智能体的风险感知决策策略
Risk-Aware Decision Policies for Agents Under Noisy Perception
- University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对感知噪声下智能体决策问题，该研究构建人工生命捕食者-猎物觅食模型，发现不确定性感知策略可提升智能体存活率，揭示了行为随不确定性的状态转变，为含噪声标签的鲁棒学习提供类比。
AI中文摘要：
生物系统的感知天生带有噪声，这使得生物必须在误分类代价高昂甚至致命的不确定性下做出决策。我们提出一种人工生命捕食者-猎物觅食模型，该模型适用于感知噪声场景，并比较了智能体使用考虑自身噪声预测的各类策略时的性能。通过在对称与非对称感知噪声下开展受控实验，我们发现盲目信任感知标签会随着噪声增大导致灾难性失败，而感知不确定性策略能显著提升存活率并减少致命错误。我们还观察到行为的定性状态转变：随着不确定性增加，智能体从探索策略转向保守策略。该模型通过证明在感知不可靠时，明确的信息收集可提升鲁棒性，将风险敏感觅食、生态信息利用与人工生命联系起来。这些结果凸显了不确定性感知决策的重要性，并为含噪声标签的鲁棒学习提供了可解释的人工生命类比。
英文摘要：
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.
