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..
An Axiomatic Approach to Revising Preferences
Authors: Adrian Haret, Johannes Peter Wallner5676-5683
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study a model of preference revision in which a prior preference over a set of alternatives is adjusted... We analyze this model under two aspects: the first allows us to capture natural distance-based operators... Requiring the input and output to be aligned yields a second type of operator, which we characterize using preferences on the comparisons in the prior preference. Preference revision is set in a logic-based framework and using the formal machinery of belief change... we propose rationality postulates for each of the two versions of our model and derive representation results... |
| Researcher Affiliation | Academia | 1 Institute for Logic, Language and Computation (ILLC), University of Amsterdam, The Netherlands 2 Institute of Software Technology, Graz University of Technology, Austria |
| Pseudocode | No | The paper describes procedures like the 'addi' operator in text and with a diagram (Figure 2), but does not provide formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples rather than empirical datasets for training. Therefore, no information on publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for reproducibility of empirical experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific setup details like hyperparameters or training configurations. |