Considering Constraint Monotonicity and Foundedness in Answer Set Programming
Authors: Yi-Dong Shen, Thomas Eiter
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we address the question by introducing natural logic programs whose expected answer sets and world views violate these properties and thus may be viewed as counter-examples to these requirements. Specifically we use instances of the generalized strategic companies problem for ASP benchmark competitions as concrete examples to demonstrate that the requirements of constraint monotonicity and foundedness may exclude expected answer sets for some simple disjunctive programs and world views for some epistemic specifications. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China 2Institute of Logic and Computation, Vienna University of Technology (TU Wien), Favoritenstraße 9-11, A-1040 Vienna, Austria |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the described methodology. |
| Open Datasets | No | The paper uses instances of the Generalized Strategic Companies (GSC) problem as concrete examples for theoretical analysis, not as a dataset for empirical training. No public dataset or its access information is provided. |
| Dataset Splits | No | The paper does not describe an experimental setup with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |