Intelligent Planning and Execution of Tasks Using Hybrid Agents
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
Multi agent systems are being applied to a wide range of applications from territorial explorations to unmanned combat missions. Agents operate as distributed problem solvers and work together to accomplish the tasks in the system. Various control frameworks have been developed for execution of tasks by agents in the system. Control frameworks consider that the agents are aware of their tasks and provide for the controls to complete the tasks in the dynamic, changing and uncertain environment of the system. But tasks in multi agent systems are not always predetermined and may evolve over time requiring dynamic planning for not only the controls but also the determination of tasks for the agents. This paper presents an integrated task planning and control framework for multi agent systems. A dynamic planning framework is developed which in turn is integrated with a selected hybrid control framework for multi agent systems. Simulation result are presented for the effectiveness of the proposed work towards a comprehensive solution to the multi agent systems based applications.
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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 | 0.000 | 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