A Syntax-Independent Approach to Forgetting in Disjunctive Logic Programs
Authors: James Delgrande, Kewen Wang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide a prototype implementation of this approach to forgetting. ... We investigate some optimisation techniques for the algorithm and report a prototype implementation of the algorithm. ... Our experiments on the efficiency of the system show that it can be used to efficiently handle SE-forgetting in large logic programs. |
| Researcher Affiliation | Academia | James P. Delgrande School of Computing Science Simon Fraser University Burnaby, B.C. V5A 1S6 Canada jim@cs.sfu.ca Kewen Wang School of Information and Communication Technology Griffith University, Brisbane, QLD 4111 Australia k.wang@griffith.edu.au |
| Pseudocode | Yes | Algorithm 1 (Computing a result of forgetting) Input: Disjunctive program P and atom a. Output: Forget(P, a). Procedure: Step 1. Remove tautologies, contradictory rules, and nonminimal rules from P. The resulting disjunctive program is still denoted P. |
| Open Source Code | Yes | A prototype implementation of SE-forgetting has been implemented in Java and is available at http://1drv.ms/ 1s NNCl N. |
| Open Datasets | No | The paper mentions 'Our experiments on the efficiency of the system show that it can be used to efficiently handle SE-forgetting in large logic programs.' However, it does not provide concrete access information (link, DOI, citation) for these 'large logic programs' or any other dataset used in experiments. The example used (Example 2) is illustrative and not a public dataset. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits. The experiments described relate to the efficiency of the implemented algorithm rather than model training on partitioned datasets. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper states that the prototype was 'implemented in Java'. However, it does not specify the version of Java or any other software dependencies (e.g., libraries or solvers) with specific version numbers needed for replication. |
| Experiment Setup | No | The paper describes Algorithm 1 for computing forgetting, but it does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for the efficiency experiments mentioned. |