Distributed Decision-Making and Task<br />Coordination in Dynamic, Uncertain and<br />Real-Time Multiagent Environments
Bibliographic record
Abstract
Decision-making in uncertainty and coordination are at the heart of multiagent systems. In this kind of systems, agents have to be able to perceive their environment and take decisions while considering the other agents. When the environment is partially observable, agents have to be able to manage this uncertainty in order to take the most enlightened decisions they can based on the incomplete information they have acquired. Moreover, in the context of cooperative multiagent environments, agents have to coordinate their actions in order to accomplish complex tasks requiring more then one agent. In this thesis, we consider complex cooperative multiagent environments (dynamic, uncertain and real-time). In this kind of environments, we propose an approach of decision-making in uncertainty that enable the agents to flexibly coordinate themselves. More precisely, we present an online algorithm for partially observable Markov decision processes (POMDPs). Furthermore, in such complex environments, agent's tasks can also become quite complex. In this context, it could be complicated for the agents to determine the required number of resources to accomplish each task. To address this problem, we propose a learning algorithm to learn the number of resources necessary to accomplish a task based on the characteristics of this task. In a similar manner, we propose a scheduling approach enabling the agents to schedule their tasks in order to maximize the number of tasks that could be accomplish in a limited time. All these approaches have been developed to enable the agents to efficiently coordinate all their complex tasks in a partially observable, dynamic and uncertain multiagent environment. All these approaches have demonstrated their effectiveness in tests done in the RoboCupRescue simulation environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".