LPMLN, Weak Constraints, and P-log
Authors: Joohyung Lee, Zhun Yang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper investigates the relationships between LPMLN and two other extensions of answer set programs: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. We present a translation of LPMLN into programs with weak constraints and a translation of P-log into LPMLN, which complement the existing translations in the opposite directions. The first translation allows us to compute the most probable stable models (i.e., MAP estimates) of LPMLN programs using standard ASP solvers. This result can be extended to other formalisms, such as Markov Logic, Prob Log, and Pearl s Causal Models, that are shown to be translatable into LPMLN. The second translation tells us how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers and MLN solvers. |
| Researcher Affiliation | Academia | Joohyung Lee and Zhun Yang School of Computing, Informatics and Decision Systems Engineering Arizona State University, Tempe, USA {joolee, zyang90}@asu.edu |
| Pseudocode | No | The paper describes translations using logical rules and formulas, but these are not formatted as pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its own source code. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for empirical evaluation or training. |
| Dataset Splits | No | The paper is theoretical and does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | Yes | The corollary allows us to compute the most probable stable models (MAP estimates) of the LPMLN program using the combination of F2LP 4 and CLINGO 5 (assuming that the weights are approximated to integers). |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. |