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..
Modular Systems with Preferences
Authors: Alireza Ensan, Eugenia Ternovska
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a versatile framework for combining knowledge bases in modular systems with preferences. In our formalism, each module (knowledge base) can be specified in a different language. We define the notion of a preference-based modular system that includes a formalization of metapreferences. We prove that our formalism is robust in the sense that the operations for combining modules preserve the notion of a preference-based modular system. Finally, we formally demonstrate correspondences between our framework and the related preference formalisms of cp-nets and preference-based planning. |
| Researcher Affiliation | Academia | Alireza Ensan and Eugenia Ternovska Simon Fraser University Canada EMAIL |
| 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 or refer to any publicly available datasets for training or evaluation. |
| 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 specify any hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, such as hyperparameters or training configurations. |