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Explainability Framework for Policy-Aware Autonomous Agents

arXiv AI/CL/LG（论文）T2
19:2207/23, 07:22 PMPaperRead the original ↗Export MD ↓

Score

70
Information gain7 ×0.3

提出了面向策略约束Agent的可解释框架，结合反事实逻辑与ASP实现，方法较新颖。

Content quality8 ×0.2

框架设计有社会科学理论支撑，实现细节清晰，并通过调查评估。

Practical value7 ×0.2

对于需要遵守复杂策略的自主系统（如自动驾驶）具有实际应用价值。

Timeliness10 ×0.15

论文2025年7月发布，内容非常前沿。

Source reliability8 ×0.15

来自arXiv预印本，但论文结构完整，引用规范。

Base 78 × T2 Source weight ×0.9 = 70

Curation line 70Selected

Why selected

The paper presents a concrete implementation and evaluation with clear contrastive explanation method, but the framework is limited to policy-constrained scenarios, reducing generality.

AI commentary

「What makes this paper noteworthy is its successful transfer of social science explanation theory to AI agents, elegantly implementing counterfactual reasoning in ASP. For developers working on safety-critical autonomy like self-driving cars or service robots, this framework offers a practical path to explainability. Even if you don't use ASP, the core idea of contrastive explanation is worth adopting.」

Quick overview

This paper proposes an explainability framework for policy-aware autonomous agents, leveraging social science insights and implemented using Answer Set Programming (ASP) with Python. By detecting penalty triggers for policy violations, it identifies counterfactual events to generate contrastive explanations (e.g., "the agent performed this action because otherwise undesirable event X would have occurred"). A human survey evaluates the comprehensibility of the generated explanations.

  • Proposes an explainability framework for policy-constrained agents integrating social science explanation theory
  • Generates contrastive explanations via counterfactual reasoning, using penalty signals to identify undesirable events
  • Implements with ASP for knowledge representation and Python for information extraction and NL translation
  • Evaluates explanation quality through human survey, confirming framework effectiveness

Full text

This is an AI-generated Chinese digest; use "View original" for the source text.

This paper addresses the lack of explainability in increasingly prevalent autonomous agents (e.g., self-driving cars, service robots) by proposing a framework tailored for policy-aware agents. Drawing on social science insights about effective explanations (e.g., contrastiveness, causality), the authors encode agent policies and knowledge using Answer Set Programming (ASP) and generate natural-language explanations via Python. The key innovation leverages penalty signals from policy violations to identify 'undesirable events' in counterfactual scenarios, producing contrastive explanations such as 'the agent performed this action because, had it not, event X would have occurred.' A human-subject survey evaluates the comprehensibility and satisfaction of the generated explanations, demonstrating the framework's effectiveness in helping users understand agent reasoning, particularly in safety-critical policy-compliance settings.

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