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].
Revising Beliefs and Intentions in Stochastic Environments
Authors: Nima Motamed, Natasha Alechina, Mehdi Dastani, Dragan Doder
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we initiate the study of belief and intention revision in stochastic environments, where an agent s beliefs and intentions are specified in a decidable probabilistic temporal logic. We then provide general Katsuno & Mendelzon-style representation theorems for both belief and intention revision, giving clear semantic characterizations of revision methods. |
| Researcher Affiliation | Academia | 1Utrecht University, The Netherlands 2Open University, The Netherlands EMAIL |
| Pseudocode | No | The paper presents theoretical definitions, logic syntax, semantics, and proofs, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a theoretical work and does not mention releasing any source code for its described methodologies. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers required for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |