Probabilistic Logic Programming with Beta-Distributed Random Variables
Authors: Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Şensoy7769-7776
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Analysis To evaluate the suitability of using Sβ in a Prob Log for uncertain probabilistic reasoning, we run an experimental analysis involving several a Prob Log programs with unspecified labelling function. |
| Researcher Affiliation | Collaboration | Federico Cerutti Cardiff University Cardiff UK Lance Kaplan Army Research Laboratory Adelphi, MD USA Angelika Kimmig Cardiff University Cardiff UK Murat S ensoy Ozyeˇgin University Istanbul Turkey |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to the availability of 'a Prob Log' (https://dtai.cs.kuleuven.be/problog/), which is a third-party tool used in their work, but does not state that the code for their own proposed methodology (new operators or parametrisation) is open source or publicly available. |
| Open Datasets | No | For each program, first labels are derived for Sp by selecting the ground truth probabilities from a uniform random distribution. Then, for each label of the a Prob Log program over Sp, we derive a subjective opinion by observing Nins instantiations of the random variables comprising the a Prob Log program over Sp so to simulate data sparsity (Kaplan and Ivanovska 2018). |
| Dataset Splits | No | This process of inference to determine the marginal Beta distributions is repeated 1000 times by considering 100 random choices for each label of the a Prob Log with Sp, i.e. the ground truth, and for each ground truth 10 repetitions of sampling the interpretations used to derive the subjective opinion labels used in SSL and Sβ observing Nins instantiations of all the variables. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'a Prob Log' and Subjective Logic operators but does not specify version numbers for any software components or dependencies used in the experiments. |
| Experiment Setup | No | For each program, first labels are derived for Sp by selecting the ground truth probabilities from a uniform random distribution. Then, for each label of the a Prob Log program over Sp, we derive a subjective opinion by observing Nins instantiations of the random variables comprising the a Prob Log program over Sp so to simulate data sparsity (Kaplan and Ivanovska 2018). We then proceed analysing the inference on specific query nodes q in the presence of a set of evidence E e using a Prob Log with SSL and Sβ over the subjective opinion labels, and compare the RMSE to the actual ground truth of using a Prob Log with Sp. |