Regret-Based Multi-Agent Coordination with Uncertain Task Rewards
Authors: Feng Wu, Nicholas Jennings
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 fw6e11@ecs.soton.ac.uk Nicholas R. Jennings School of Electronics and Computer Science University of Southampton, United Kingdom nrj@ecs.soton.ac.uk |
| 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. |