---
title: "Responsibility Anticipation and Attribution in LTLf"
authors:
  - "Giuseppe De Giacomo"
  - "Emiliano Lorini"
  - "Timothy Parker"
  - "Gianmarco Parretti"
doi: "10.24963/ijcai.2025/6"
url: "https://ijcai.org/proceedings/2025/6"
pdf: "https://ijcai.org/proceedings/2025/0006.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "47-55"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: KRR: Reasoning about actions"
  - "AI Ethics, Trust, Fairness: ETF: Moral decision making"
  - "Agent-based and Multi-agent Systems: MAS: Formal verification, validation and synthesis"
  - "Agent-based and Multi-agent Systems: MAS: Agent theories and models"
---

# Responsibility Anticipation and Attribution in LTLf

Giuseppe De Giacomo, Emiliano Lorini, Timothy Parker, Gianmarco Parretti

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 47-55. DOI: [10.24963/ijcai.2025/6](https://doi.org/10.24963/ijcai.2025/6)

## Abstract

Responsibility is one of the key notions in machine ethics and in the area of autonomous systems. It is a multi-faceted notion involving counterfactual reasoning about actions and strategies. In this paper, we study different variants of responsibility for LTLf outcomes based on strategic reasoning. We show a connection with notions in reactive synthesis, including the synthesis of winning, dominant, and best-effort strategies. This connection provides a strong computational grounding of responsibility, allowing us to characterize the worst-case computa- tional complexity and devise sound, complete, and optimal algorithms for anticipating and attributing responsibility.