Forgetting in Multi-Agent Modal Logics
Authors: Liangda Fang, Yongmei Liu, Hans van Ditmarsch
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |