A bayesian approach to multiagent reinforcement learning and coalition formation under uncertainty
Bibliographic record
Abstract
Sequential decision making under uncertainty is always a challenge for autonomous agents populating a multiagent environment, since their behaviour is inevitably influenced by the be-haviour of others. Further, agents have to constantly struggle to find the right balance between exploiting current information regarding the environment and the rest of its inhabitants, and ex-ploring so that they acquire additional information. Moreover, they need to profitably trade off short-term rewards with anticipated long-term ones, while learning through interaction about the environment and others—employing techniques from reinforcement learning (RL), a fun-damental area of study within artificial intelligence (AI). Coalition formation is a problem of great interest within game theory and AI, allowing autonomous individually rational agents to form stable or transient teams (or coalitions) to tackle an underlying task. Agents participating in realistic scenarios of repeated coalition formation under uncertainty face the issues identified above, and need to bargain to succesfully negotiate the terms of their participation in coalitions—often having to compromise individual with team welfare effectively. In this thesis, we provide theoretical and algorithmic tools to accommodate sequential de-
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".