用多智能体协作让视觉推理过程透明可懂。
Transparent Visual Reasoning via Object-Centric Agent Collaboration
- 基于物体中心表示与博弈论推理，实现可解释的决策过程。
- 在两个多物体数据集上表现媲美顶尖黑盒模型。
- 用户研究证实其解释更直观可信，适合医疗等高风险场景。
可解释人工智能的核心挑战之一是生成基于人类可理解概念的解释。为此，我们提出OCEAN（基于智能体协商的物体中心解释框架），一个基于物体中心表征和透明多智能体推理过程的全新可解释框架。博弈论推理促使智能体就一致且有区分性的证据达成共识，从而实现忠实且可解释的决策过程。我们在两个诊断型多物体数据集上端到端训练OCEAN，并与标准视觉分类器及流行的后验解释工具（如GradCAM、LIME）进行对比。结果表明，OCEAN在性能上媲美最先进黑盒模型，且推理过程真实可信。用户研究表明，参与者普遍认为OCEAN的解释更直观、更值得信赖。
原文摘要 · Abstract (English)
A central challenge in explainable AI, particularly in the visual domain, is producing explanations grounded in human-understandable concepts. To tackle this, we introduce OCEAN (Object-Centric Explananda via Agent Negotiation), a novel, inherently interpretable framework built on object-centric representations and a transparent multi-agent reasoning process. The game-theoretic reasoning process drives agents to agree on coherent and discriminative evidence, resulting in a faithful and interpretable decision-making process. We train OCEAN end-to-end and benchmark it against standard visual classifiers and popular posthoc explanation tools like GradCAM and LIME across two diagnostic multi-object datasets. Our results demonstrate competitive performance with respect to state-of-the-art black-box models with a faithful reasoning process, which was reflected by our user study, where participants consistently rated OCEAN's explanations as more intuitive and trustworthy.
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