Distributed Decision-Making and Task<br />Coordination in Dynamic, Uncertain and<br />Real-Time Multiagent Environments
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Bibliographic record
Abstract
Decision-making in uncertainty and coordination are at the heart of multiagent<br />systems. In this kind of systems, agents have to be able to perceive their environment<br />and take decisions while considering the other agents. When the environment is partially<br />observable, agents have to be able to manage this uncertainty in order to take the<br />most enlightened decisions they can based on the incomplete information they have<br />acquired. Moreover, in the context of cooperative multiagent environments, agents<br />have to coordinate their actions in order to accomplish complex tasks requiring more<br />then one agent.<br />In this thesis, we consider complex cooperative multiagent environments (dynamic,<br />uncertain and real-time). In this kind of environments, we propose an approach of<br />decision-making in uncertainty that enable the agents to flexibly coordinate themselves.<br />More precisely, we present an online algorithm for partially observable Markov decision<br />processes (POMDPs).<br />Furthermore, in such complex environments, agent's tasks can also become quite<br />complex. In this context, it could be complicated for the agents to determine the<br />required number of resources to accomplish each task. To address this problem, we<br />propose a learning algorithm to learn the number of resources necessary to accomplish<br />a task based on the characteristics of this task. In a similar manner, we propose a<br />scheduling approach enabling the agents to schedule their tasks in order to maximize<br />the number of tasks that could be accomplish in a limited time.<br />All these approaches have been developed to enable the agents to efficiently coordinate<br />all their complex tasks in a partially observable, dynamic and uncertain multiagent<br />environment. All these approaches have demonstrated their effectiveness in tests done<br />in the RoboCupRescue simulation environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it