Discounting in Strategy Logic
Authors: Munyque Mittelmann, Aniello Murano, Laurent Perrussel
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we augment Strategy Logic with future discounting over a set of discounted functions D, denoted SLdisc[D]. We consider until operators with discounting functions: the satisfaction value of a specification in SLdisc[D] is a value in [0, 1], where the longer it takes to fulfill requirements, the smaller the satisfaction value is. We motivate our approach with classical examples from Game Theory and study the complexity of model-checking SLdisc[D]-formulas. |
| Researcher Affiliation | Academia | Munyque Mittelmann1 , Aniello Murano1 and Laurent Perrussel2 1University of Naples Federico II 2University of Toulouse IRIT {munyque.mittelmann, aniello.murano}@unina.it, laurent.perrussel@irit.fr |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm,' nor does it present structured algorithm blocks. |
| Open Source Code | No | The paper does not include any statements about providing open-source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | No | The paper is theoretical, defining a new logic and analyzing its complexity. It uses conceptual examples from Game Theory (e.g., Secretary Problem, Negotiation), but these are illustrative and do not involve empirical data or datasets. No public datasets, URLs, DOIs, or specific citations for empirical data sources are provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data. Therefore, there are no mentions of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical, focusing on logical frameworks and complexity analysis. There is no mention of specific hardware (e.g., GPU models, CPU types, cloud resources) used for any computations or analyses. |
| Software Dependencies | No | The paper is theoretical, focusing on logical frameworks and complexity analysis. It does not mention any specific software packages, libraries, or their version numbers that would be necessary for reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments. Therefore, there are no details provided regarding experimental setup, hyperparameters, or system-level training settings. |