Distributed Gradient Optimization with Embodied Approximation
Bibliographic record
Abstract
We present an informal description of a general approach for developing decentralized distributed gradient descent optimization algorithms for teams of embodied agents that need to rearrange their configuration over space and/or time, into some optimal and initially unknown configuration. Our approach relies on using embodiment and spatial embeddedness as a surrogate for computational resources, permitting the reduction or elimination of communication or shared memory for conventional parallel computation. Intermediate stages of the gradient descent process are manifested by the locations of the robots, instead of being represented symbolically. Each point in the space-time evolution of the system can be considered an approximation of the solution, which is refined by the agents ’ motion in response to sensor measurements. For each agent, motion is approximately in the direction of the local antigradient of the global cost function. We illustrate this approach by giving solutions to two non-trivial realistic optimization tasks from the robotics domain. We suggest that embodied approximations can be used by living distributed systems to find affordable solutions to the optimization tasks they face.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".