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..
Forgetting in Multi-Agent Modal Logics
Authors: Liangda Fang, Yongmei Liu, Hans van Ditmarsch
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study forgetting in multi-agent modal logics. We adopt the semantic definition of existential bisimulation quantifiers as that of forgetting. We propose a syntactical way of performing forgetting based on the canonical formulas of modal logics introduced by Moss. We show that the result of forgetting a propositional atom from a satisfiable canonical formula can be computed by simply substituting the literals of the atom with >. Thus we show that Kn, Dn, Tn, K45n, KD45n and S5n are closed under forgetting, and hence have uniform interpolation. |
| Researcher Affiliation | Academia | Liangda Fang1,3 Yongmei Liu1 Hans van Ditmarsch2 1Dept. of Computer Science, Sun Yat-sen University, Guangzhou 510006, China 2LORIA, CNRS Universit e de Lorraine, France 3Dept. of Computer Science, Jinan University, Guangzhou 510632, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets. Thus, no information regarding public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets. Therefore, no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper focuses on theoretical aspects and does not report on experimental setups that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |