Rational aggressive behaviour reduces interference in a mobile robot team
Why this work is in the frame
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Bibliographic record
Abstract
Spatial interference can reduce the effectiveness of teams of mobile robots. We examine a team of robots with no centralized control performing a transportation task, in which robots frequently interfere with each other. The robots must work in the same space, so territorial methods are not appropriate. Previously we have shown that a stereotyped competition, inspired by aggressive displays in various animal species, can reduce interference and improve overall system performance. However, none of the methods previously devised for selecting a robot's 'aggression level' performed better than selecting aggression at random. This paper describes a new, principled approach to selecting an aggression level, based on robot's investment in a task. Simulation experiments with teams of six robots in an office-type environment show that, under certain conditions, this method can significantly improve system performance compared to a random competition and a noncompetitive control experiment. Finally, we discuss the benefits and limitations of such a scheme with respect to the specific 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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