Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Responsibility Anticipation and Attribution in LTLf
Authors: Giuseppe De Giacomo, Emiliano Lorini, Timothy Parker, Gianmarco Parretti
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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 computational complexity and devise sound, complete, and optimal algorithms for anticipating and attributing responsibility. We prove membership of checking active responsibility by exhibiting a sound and complete algorithm to solve it. |
| Researcher Affiliation | Academia | Giuseppe De Giacomo1,2 , Emiliano Lorini3 , Timothy Parker3 and Gianmarco Parretti2 1University of Oxford 2University of Rome La Sapienza 3IRIT, CNRS, Toulouse University, France EMAIL, EMAIL, EMAIL EMAIL |
| Pseudocode | Yes | We begin by giving an algorithm to check if a strategy σag is winning for φ under E, denoted CHECKWIN(φ, E, σag): 1. Construct the NFA N φ of φ, the DFA AE of E, and the DFA Aσag of σag; 2. Restrict AE to the environment winning region and obtain DFA A E; and 3. Check language nonemptiness of the product N = N φ A E Aσag. |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing code, nor does it provide links to code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. The examples provided (e.g., “The plant is watered”) are illustrative scenarios and do not refer to actual datasets. |
| Dataset Splits | No | The paper does not involve empirical experiments using datasets, therefore, no dataset splits are discussed. |
| Hardware Specification | No | The paper focuses on theoretical contributions, computational complexity, and algorithm design, without describing any experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and complexity analysis but does not mention specific software dependencies with version numbers used for implementing or executing experiments. |
| Experiment Setup | No | The paper is theoretical and focuses on formalizing concepts, analyzing complexity, and designing algorithms. It does not describe any empirical experimental setup, hyperparameters, or training configurations. |