LTL
Authors: f
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We start by showing that the usual belief-state construction used in planning under partial observability works also for general LTLf/LDLf synthesis, though with a jump in computational complexity from 2EXPTIME to 3EXPTIME. Then we show that the belief-state construction can be avoided in favor of a direct automata construction which exploits projection to hide unobservable propositions. This allow us to prove that the problem remains 2EXPTIME-complete. |
| Researcher Affiliation | Academia | Giuseppe De Giacomo Sapienza Universit a di Roma Roma, Italy degiacomo@dis.uniroma1.it Moshe Y. Vardi Rice University, Houston, TX, USA vardi@cs.rice.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating access to source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not mention any dataset splits (training, validation, or test) for experimental reproduction. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |