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..
In Praise of Belief Bases: Doing Epistemic Logic Without Possible Worlds
Authors: Emiliano Lorini
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a new semantics for a logic of explicit and implicit beliefs based on the concept of multi-agent belief base. Differently from existing Kripke-style semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are deο¬ned from the concept of belief base. We provide a complete axiomatization and a decidability result for our logic. |
| Researcher Affiliation | Academia | Emiliano Lorini CNRS-IRIT, Toulouse University, France |
| Pseudocode | No | The paper does not contain 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 or reference any publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific ancillary software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details, such as hyperparameters or training configurations. |