Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Probabilistic Belief Contraction Using Argumentation
Authors: Kinzang Chhogyal, Abhaya Nayak, Zhiqiang Zhuang, Abdul Sattar
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,284 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,736 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first classify different belief states by their stability, and then exploit the quantitative nature of probabilities and combine it with the basic ideas of argumentation theory to determine the mixture proportions. We, therefore, propose a novel approach to probabilistic belief contraction using argumentation. (...) Section 2 introduces probabilistic belief states and Section 3 briefly reviews probabilistic belief contraction and its problems. In Section 4, we give an overview of argumentation theory and our argumentation framework for determining the mixture proportion is presented in Section 5 followed by the discussion and conclusion in Section 6. |
| Researcher Affiliation | Academia | 1 Griffith University, Brisbane, Australia | 2 Macquarie University, Sydney, Australia |
| Pseudocode | No | The paper describes theoretical concepts and calculations using mathematical notation and natural language but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or mention any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, or test) as no empirical experiments are conducted. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or versions for implementation or experimentation. |
| Experiment Setup | No | The paper describes a theoretical framework and does not include details about an experimental setup, hyperparameters, or training configurations. |