---
title: "Explainability Framework for Policy-Aware Autonomous Agents"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.21209v1"
permalink: "https://feed.astralor.com/items/itm_239aca1b136af24dc4e17348"
published: "2026-07-23T11:22:25.000Z"
score: 70
channel: "tech"
tags: ["Explainable AI","Answer Set Programming","Contrastive Explanation","Autonomous Agent"]
---

# Explainability Framework for Policy-Aware Autonomous Agents

## 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.

## At a glance

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.

## Key points

- 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

In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal. As these systems grow more prevalent in our day-to-day lives, there has been an increased need to add explainability features which can provide an account for an agent's behavior. We therefore propose a framework that outlines how to produce comprehensible explanations for policy-aware agents, or agents which have rule-enforcing policies incorporated in their decision-making framework. This framework is designed using insights from the social sciences on how to produce good explanations. It is implemented in the Answer Set Programming language while using Python to assist with information extraction and natural-language translation. Because these agents incur penalties when violating policies, we are able to leverage these penalties to detect undesirable events in scenarios that are counterfactual to the agents' original actions. This lends itself to creating contrastive explanations (e.g., "the agent performed this action because, had it not, undesirable event X would have occurred."), which formulate the core component for our explainability framework. The framework is evaluated using a survey wherein human participants provide feedback on our program-generated explanations.

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Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_239aca1b136af24dc4e17348