A Logical Analysis of Hanabi

Authors: Elise Perrotin

AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical A Logical Analysis of Hanabi. In this paper we have extended the epistemic logic EL-O with infinite semantics accounting for common knowledge within arbitrary groups of agents. We have then proposed a formalization of Hanabi in this generalized setting, which relies on a special reasoning action to make up for the limited reasoning capabilities of agents in EL-O. Finally, we have shown that Hanabi can actually be captured in a much simpler, finite fragment of the full framework.
Researcher Affiliation Academia Elise Perrotin AIST, Tokyo, Japan EMAIL
Pseudocode No The paper uses formal logical notation to describe preconditions and effects of actions (e.g., "Formally, the precondition of Give Hint(a, b, A) is: Turna Reason _ k>0 Hint Tokenk."), and descriptive numbered lists for reasoning steps (e.g., "The reasoning goes as follows: 1. If e.g. all yellow 3s and 4s are now jointly seen by some group G..."), but it does not contain structured pseudocode or algorithm blocks with code-like formatting.
Open Source Code No The paper does not provide any specific statement about open-sourcing code, a repository link, or mention code in supplementary materials for the methodology described.
Open Datasets No The paper analyzes the card game Hanabi from a logical perspective and does not use or refer to any specific datasets for empirical evaluation.
Dataset Splits No The paper is theoretical and does not involve empirical evaluation with datasets, thus no dataset splits are mentioned.
Hardware Specification No The paper is a theoretical analysis and does not describe any experimental setup or mention specific hardware used.
Software Dependencies No The paper is theoretical and does not mention any specific software dependencies or versions.
Experiment Setup No The paper describes a theoretical formalization of Hanabi in epistemic logic and does not include details on experimental setup, hyperparameters, or training configurations.