Situation Calculus Temporally Lifted Abstractions for Generalized Planning
Authors: Giuseppe de Giacomo, Yves Lespérance, Matteo Mancanelli
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our approach by synthesizing a program that solves a data structure manipulation problem. We illustrate how our approach works by using it to synthesize a program to find the minimum value of a list. Figure 1 shows the controller obtained by using the engine Strix (Meyer, Sickert, and Luttenberger 2018), as done by (B20). |
| Researcher Affiliation | Academia | 1University of Oxford, Oxford, UK 2University of Rome La Sapienza, Rome, Italy 3York University, Toronto, ON, Canada |
| Pseudocode | Yes | Figure 1: Controller for finding the minimum in a list. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the methodology described. |
| Open Datasets | No | The paper illustrates its approach using common programming problems and data structures, such as finding the minimum value in a singly-linked list, but does not refer to or provide access to any specific public datasets. |
| Dataset Splits | No | The paper does not conduct experiments on specific datasets requiring explicit training/test/validation splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running any experiments or synthesising programs. |
| Software Dependencies | No | The paper mentions using the 'Strix' engine for LTL synthesis, but does not provide a specific version number for this software or any other key software components used in the methodology. |
| Experiment Setup | No | The paper illustrates its approach with an example and mentions LTL synthesis, but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or training configurations for the synthesis process. |