Decision assistance agent in real-time simulation
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
Urban society relies heavily on critical infrastructure (CI) such as power and water systems. The anticipated prosperity and the national security of society depend on the ability to understand, measure and analyse the vulnerabilities and interdependencies of this system of infrastructures. Only then can emergency responders (ER) react quickly and effectively to any major disruption that the system might face. In this paper, we propose a model to train a reinforcement learning (RL) agent that is able to optimise resource usage following an infrastructure disruption. The novelty of our approach is the use of dynamic programming techniques to build an agent that is able to learn from experience, where the experience is generated by a simulator. The goal of the agent is to maximise an output, which in our case is the number of discharged patients (DP) from hospitals or on-site emergency units. We show that by exposing such an intelligent agent to a large sequence of simulated disaster scenarios, we can capture enough experience to enable the agent to make informed decisions.
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 | 0.001 |
| 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 it