INFLUENCE OF TEMPERATURE ON SWARMBOTS THAT LEARN
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
The problem considered in this article is how a cybernetic system can learn to control its actions in a hostile environment. This article focuses on an approach to solving this problem in an environment with varying temperatures. In effect, machines that operate outdoors have higher survivability if actions are chosen during periods when it is cooler (e.g., night-time or early morning rather than mid- to late afternoon during summer months). The assumption made here is that learning to choose actions that compensate for the influence of temperature has beneficial influence on the functioning of individuals in robot societies (collections of cooperating robots called swarmbots or swarms). In keeping with this idea, a biologically-inspired form of adaptive learning is given in this article. Conventional actor-critic learning provides a framework for the control strategy introduced in this article. It is ethology (study of behavior of organisms) that provides a basis for monitoring the behavior of a swarmbot. Individual behaviours together with sensor measurements are recorded in tables called ethograms. Swarm behavior tends to be episodic. An ethogram is recorded during each episode during the lifespan of a swarm. Each ethogram is a source of measurements that can be used to influence learning during an episode. The contribution of this article is the introduction of a biologically-inspired approach to learning that adapts to changing temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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 it