Distributed Autoepistemic Logic and its Application to Access Control
Authors: Pieter Van Hertum, Marcos Cramer, Bart Bogaerts, Marc Denecker
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we define and study an extension of autoepistemic logic (AEL) called distributed autoepistemic logic (d AEL) with multiple agents that have full introspection in their own knowledge as well as in that of others. We define 2and 3-valued semantic operators for d AEL. Using these operators, approximation fixpoint theory, an abstract algebraic framework that unifies different knowledge representation formalisms, immediately yields us a family of semantics for d AEL, each based on different intuitions that are wellstudied in the context of AEL. In this paper we study the semantics of d AEL(ID), but for practical applications, a decision procedure for d AEL(ID) or an expressively rich subset of it needs to be developed. The complexity of determining access rights based on a theory written in d AEL(ID) should be studied. |
| Researcher Affiliation | Academia | Pieter Van Hertum KU Leuven Leuven, Belgium pieter.vanhertum@cs.kuleuven.be Marcos Cramer University of Luxembourg Luxembourg, Luxembourg marcos.cramer@uni.lu Bart Bogaerts Aalto University Espoo, Finland bart.bogaerts@aalto.fi Marc Denecker KU Leuven Leuven, Belgium marc.denecker@cs.kuleuven.be |
| Pseudocode | No | The paper defines logical systems and their semantics using formal definitions and mathematical notation, but it does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described logic or its implementation. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. The examples provided are illustrative, not empirical data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data or dataset splits for training or validation. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper defines a logical system and analyzes it formally; it does not mention specific software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus there are no experimental setup details, hyperparameters, or training settings to report. |