Combining Logic and Probability: P-log Perspective
Authors: Evgenii Balai
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In the thesis I define a class of programs which extend the class used in the algorithm from [Zhu, 2012], prove their coherency (an important property defined in [Baral et al., 2009]) and design a new inference algorithm which works for programs in this class. So far I have: [...] designed a pseudo-code for a new inference algorithm; [...] Future work related to my thesis will consist of : proving the correctness of the designed algorithm, implementing it and investigating the efficiency; |
| Researcher Affiliation | Academia | Evgenii Balai Texas Tech University, Lubbock, Texas evgenii.balai@ttu.edu |
| Pseudocode | Yes | Algorithm 1 Inference in P-log Input: A P-log program , a query Q to Output: The probability of Q with respect to 1: Q := Simplify( , Q) 2: Return Compute Probability( Q, Q) |
| Open Source Code | No | The paper mentions that a framework was 'implemented' and that the author found 'errors in its pseudocode and implementation' of prior work, but there is no statement about making the author's own code or the code for the described algorithm publicly available, nor is a link provided. |
| Open Datasets | No | The paper provides an example program for illustrative purposes but does not describe the use of any publicly available datasets for training, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper does not provide any specific details regarding training, validation, or test dataset splits. The current work presented is theoretical and does not involve empirical data splitting. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or cloud computing specifications used for running experiments. |
| Software Dependencies | No | The paper refers to various software and formalisms like 'P-log', 'Problog', and mentions that a 'framework was implemented' and that errors were found in a prior 'implementation'. However, it does not specify any version numbers for these or any other software dependencies needed for replication. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings, as the work presented is primarily theoretical algorithm design. |