# Computational Grounding of Responsibility Attribution and Anticipation in LTLf

arXiv: https://arxiv.org/abs/2410.14544 · HTML: https://mlsanity.com/paper/2410.14544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [A Game-Theoretic Account of Responsibility Allocation](https://mlsanity.com/paper/2105.09129/) (2021)
- [Model Checking Strategies from Synthesis Over Finite Traces](https://mlsanity.com/paper/2305.08319/) (2023)
- [Neural Entity Summarization with Joint Encoding and Weak Supervision](https://mlsanity.com/paper/2005.00152/) (2020)

## Abstract (arXiv)

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 in a strategic setting based on LTLf. We show a connection with notions in reactive synthesis, including synthesis of winning, dominant, and best-effort strategies. This connection provides the building blocks for a computational grounding of responsibility including complexity characterizations and sound, complete, and optimal algorithms for attributing and anticipating responsibility.