Reasoning About Causal Knowledge in Nondeterministic Domains
Authors: Shakil M. Khan, Yves Lespérance, Maryam Rostamigiv
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a situation calculus-based framework for reasoning about causal knowledge in nondeterministic domains. Our contribution in this paper is three-fold. First, we give a new successor-state axiom for the knowledge-accessibility relation and show how one can model knowledge update in the NDSC, and study some of its properties. Secondly, we discuss how a recently proposed account of causation in the situation calculus can be naturally combined with this account of knowledge in the NDSC to model causal knowledge in the NDSC. Finally, we extend knowledge regression in the situation calculus and show how one can reason about causal knowledge in the NDSC. |
| Researcher Affiliation | Academia | 1Dept. of Computer Science, University of Regina, Regina, Saskatchewan, Canada 2Dept. of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It defines logical formulas, propositions, and theorems, and describes concepts and operations formally, but without a distinct pseudocode section. |
| Open Source Code | No | The paper does not mention any release of source code or provide links to a code repository. The work is theoretical and focuses on formalizing a framework. |
| Open Datasets | No | The paper describes a conceptual example involving a robot navigating between locations (Example, Section 3 and 4) to illustrate the theoretical framework. However, it does not use or refer to any specific, publicly available datasets for empirical evaluation. Therefore, no access information for datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments that would involve dataset splits. The example scenario used is conceptual and illustrative. |
| Hardware Specification | No | The paper is theoretical and focuses on developing a formal framework for reasoning about causal knowledge. It does not describe any experimental implementations or mention specific hardware used. |
| Software Dependencies | No | The paper introduces a theoretical framework based on the Situation Calculus and Nondeterministic Situation Calculus (NDSC). It does not describe any specific software implementation or list software dependencies with version numbers. |
| Experiment Setup | No | The paper presents a theoretical framework and does not include any experimental evaluation. Therefore, there are no details on experimental setup, hyperparameters, or training configurations. |