Moral Planning Agents with LTL Values

Authors: Umberto Grandi, Emiliano Lorini, Timothy Parker

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We provide complexity results for a variety of MPA problems. and Section 5 analyses the computational complexity of the comparison notions introduced in Section 4. and This section provides an analysis of the computational complexity of optimistic and pessimistic comparison, as well as the notions of blameworthiness introduced in Section 4.2. These tasks are PSPACE-complete, in line with the complexity of classical problems in planning or the model checking of LLTLf -formulas.
Researcher Affiliation Academia Umberto Grandi , Emiliano Lorini , Timothy Parker IRIT, CNRS, University of Toulouse, Toulouse, France {umberto.grandi,emiliano.lorini,timothy.parker}@irit.fr
Pseudocode No The paper describes models and definitions but does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access to source code for the methodology described, nor does it include a specific repository link or explicit code release statement.
Open Datasets No The paper uses an illustrative 'Toy-sharing' example, but it does not refer to a publicly available or open dataset for experimentation, nor does it provide concrete access information for any data.
Dataset Splits No As the paper focuses on theoretical contributions and does not conduct experiments with datasets, there is no information provided about dataset splits for training, validation, or testing.
Hardware Specification No The paper focuses on theoretical analysis and does not provide any specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments.
Software Dependencies No The paper describes theoretical frameworks and logical systems but does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate experiments.
Experiment Setup No The paper is theoretical and does not involve empirical experiments; therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations.