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..
Learning Abduction Using Partial Observability
Authors: Brendan Juba, Zongyi Li, Evan Miller
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we consider a learning to reason (Khardon and Roth 1997) or PAC-learning (Valiant 1984; 2000) formulation of the combined task of learning to abduce, introduced by Juba (2016). |
| Researcher Affiliation | Academia | Brendan Juba, Zongyi Li, Evan Miller Dept. of Computer Science and Engineering Washington University in St. Louis {bjuba, zli, evan.a.miller} @wustl.edu |
| Pseudocode | Yes | Algorithm 1: Decide PAC |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and focuses on a PAC-learning framework with conceptual 'examples drawn from the prior distribution' rather than using a specific, publicly available dataset for empirical evaluation. |
| Dataset Splits | No | As a theoretical paper, no empirical experiments with validation dataset splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and proofs, but does not describe any empirical experimental setups or hyperparameter details. |