Modeling and design monitor agent using layered control architecture
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
A software agent is defined as an autonomous software entity that can interact with its environment. It is capable of responding to other agent and/or its environment to some degree. It has some degree of control over its internal state and actions based on its own model. The behavior of an agent has been described by BDI theory as a processing cycle. According to this theory we have developed the processing cycle with software feedback mechanism. Software feedback or loop-back control mechanism is capable acting without direct external intervention. A feedback mechanism continuously monitors the output of the system under control (the target system), compares the result against preset values (goals of the feedback control) and feeds the difference back to adjust the behavior of the target system in one processing cycle. This paper considers the modeling and design of a monitor agent with layered control architecture for autonomy and adaptation. The architecture consists three layers: schedule layer, optimization layer and regulator layer. The regulator layer utilizes software feedback and control methods in a process cycle, the optimization layer will help to adapt to the changing environment more precisely, the schedule layer generates the long-term goal for the agent. Also this paper gives a example of the agent for monitoring the mail server running Lotus Notes. Such sort of computing systems typically have two competitive control goals, namely: maximization of the throughput and minimization of the response time. A set of experimental results showing the effectiveness of the monitor agent for email server has been presented.
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 | 0.000 | 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