Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Maintenance of Social Commitments in Multiagent Systems
Authors: Pankaj Telang, Munindar P. Singh, Neil Yorke-Smith11369-11377
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. By developing a rule-based operational semantics, we study the relationship between agents achievement and maintenance goals, achievement commitments, and maintenance commitments. We motivate a notion of coherence which captures alignment between an agents achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system are coherent. |
| Researcher Affiliation | Collaboration | 1 SAS Institute, Cary, NC 27513, USA 2 North Carolina State University, Raleigh, NC 27695, USA 3 Delft University of Technology, Delft, 2600GA, The Netherlands |
| Pseudocode | No | The paper describes rules (e.g., S-CREATE, A-CONSIDER) in a structured format with conditions and actions, but they are presented in prose and mathematical notation rather than as formal pseudocode blocks or algorithms. |
| Open Source Code | No | No explicit statement or link is provided for open-source code for the methodology described in the paper. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. The scenario presented in Section 7 is an illustration of the theory, not an empirical experiment with data. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not include empirical experiments with detailed setup configurations or hyperparameters. |