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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Regret-Based Multi-Agent Coordination with Uncertain Task Rewards
Authors: Feng Wu, Nicholas Jennings
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we show that our method can scale up to task allocation domains with hundreds of agents and tasks (intractable for centralized methods) and can outperform state-of-the-art decentralized approaches by having much higher values and lower regrets. |
| Researcher Affiliation | Academia | Feng Wu School of Electronics and Computer Science University of Southampton, United Kingdom EMAIL Nicholas R. Jennings School of Electronics and Computer Science University of Southampton, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1: Iterative Constraint Generation Max-Sum |
| Open Source Code | No | The paper does not provide any links to open-source code or make an explicit statement about code availability. |
| Open Datasets | No | The paper describes developing a simulator and generating tasks and states randomly for the experiments, rather than using a publicly available dataset with access information. 'We developed a simulator for the above scenario, in which tasks with any 4 types of targets (i.e., food, animal, victim, and fuel) were randomly generated on a 2D grid map.' |
| Dataset Splits | No | The paper describes setting up a simulation environment and randomizing aspects of the problem instances, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We ran our experiments on a machine with a 2.66GHZ Intel Core 2 Duo and 4GB memory. |
| Software Dependencies | Yes | All the algorithms were implemented in Java 1.6, and the linear programs are solved by CPLEX 12.4. |
| Experiment Setup | Yes | We developed a simulator for the above scenario, in which tasks with any 4 types of targets (i.e., food, animal, victim, and fuel) were randomly generated on a 2D grid map. ... we randomized the requirements of each target type and kept them fixed for each instance. ... For each state sj Sj, we specified a utility Uj(sj, xj) for the responders doing the task in a given state... For each instance, we defined a Markov chain for the states of each task with the transition matrix randomly initialized. |