Combining coordination mechanisms to improve performance in multi-robot teams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms; while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can significantly improve coordination and system performance over either approach individually. First, we use difference evaluations (which aim to compute an agent’s contribution to the team) and stigmergy to promote implicit coordination. Second, we introduce an explicit coordination mechanism dubbed intended Destination Enhanced Artificial State (IDEAS), where an agent incorporates other agents’ intended destinations directly into its state. The IDEAS approach does not require any formal negotiation between agents, and is based on passive information sharing. Finally, we combine these two approaches on a variant of a team-based multi-robot exploration domain, and show that agents using a both explicit and implicit coordination outperform other learning agents up to 25%.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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