{
  "id": 10593,
  "url": "https://arxiv.org/abs/2607.13655v1",
  "title": "Explaining Reinforcement Learning Agents via Inductive Logic Programming",
  "summary": "Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific audience and lacking shared evaluation metrics. On the other hand, logic-based approaches within eXplainable Artificial Intelligence (XAI) provide compact, human-readable abstractions of decision-making. However, the syste",
  "authors": "Celeste Veronese, Edoardo Zorzi, Daniele Meli, Alessandro Farinelli",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T10:02:55.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/10593",
  "original_url": "https://arxiv.org/abs/2607.13655v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}