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 [1].
Belief Change with Uncertain Action Histories
Authors: Aaron Hunter, James Delgrande
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution is a formal mechanism for reasoning about incorrect or weakly held beliefs related to action histories. ... We formulate all of our results in a simple transition system framework ... We proceed as follows. In Section 2, we introduce the formal preliminaries. ... We then define a general class of plausibility functions in Section 3, and we show how a sequence of plausibility functions can be used to represent an uncertain sequence of actions and observations. ... We compare our approach with related work in Section 5, and we discuss limitations and advantages in Section 6. |
| Researcher Affiliation | Academia | Aaron Hunter aaron EMAIL British Columbia Institute of Technology Burnaby, BC, Canada James P. Delgrande EMAIL Simon Fraser University Burnaby, BC, Canada |
| Pseudocode | No | The paper describes mathematical definitions and propositions (e.g., Definition 1 A transition system is a pair S, R where S 2F, R S A S., Proposition 1 Let W = ACT, OBS be a graded world view and let M be the set of pointwise minima for W. If M = , then Φ(W) = M.), and provides conceptual examples (e.g., the cookie example), but it does not include any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor does it present any structured, code-like procedural descriptions. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories or supplementary materials for code implementation. |
| Open Datasets | No | The paper introduces motivating examples such as 'Consider a simple action domain involving four agents: Bob, Alice, Eve, and Trent.' (Section 2.4) and 'Consider an action domain involving a single fluent symbol Lamp On indicating whether or not a certain lamp is turned on.' (Section 4.6) to illustrate the framework, but these are conceptual examples, not actual datasets used for experiments. There is no mention of publicly available or open datasets, nor are there any links, DOIs, or citations to data repositories. |
| Dataset Splits | No | The paper does not conduct empirical studies using datasets; therefore, there is no information provided regarding dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical in nature and does not describe any experimental evaluations that would require specific hardware. Consequently, there are no details provided about GPU models, CPU types, or other hardware specifications. |
| Software Dependencies | No | The paper is a theoretical work and does not detail any experimental implementation. As such, it does not list specific software dependencies, libraries, or solver versions. |
| Experiment Setup | No | The paper presents a formal framework and theoretical results. It does not include an experimental section and thus provides no details on hyperparameter values, training configurations, or system-level settings. |