Handling Uncertainty in Answer Set Programming
Authors: Yi Wang, Joohyung Lee
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of both formalisms in a single framework, allowing us to represent commonsense reasoning problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way. The work presented here calls for more future work. One may design a native computation algorithm for LPMLN which would be feasible to handle certain non-tight programs. |
| Researcher Affiliation | Academia | Yi Wang and Joohyung Lee School of Computing, Informatics, and Decision Systems Engineering Arizona State University, Tempe, USA {ywang485, joolee}@asu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using 'Alchemy' which is an existing implementation: 'This result allows us to use an existing implementation of MLN, such as Alchemy, to effectively compute tight LPMLN programs.' |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset. It refers to a 'probabilistic variant of the Wolf, Sheep and Cabbage puzzle' as an application example, which is a problem description, not a dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It mentions using 'Alchemy' but does not specify its version: 'used Alchemy to check that the probability of the success of this plan is p p and that of the original 17 step plan is 1.' |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. The 'experiments' mentioned are more illustrative examples or demonstrations. |