A neural network based intelligent planner for the coordinated hybrid agent framework
Bibliographic record
Abstract
Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). In the past few years, it has been applied to both homogeneous and heterogeneous multi-agent systems. In previous studies, the coordination among agents were implemented based on the designer's knowledge of the system. For large complex systems, it would be desirable if we can plan the coordination among agents dynamically. In this study, we demonstrate that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in a multiagent system. The proposed intelligent planner is based on the construction of a biologically inspired neural network. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal, permanent obstacles and temporary obstacles. Each agent treats other robots as moving obstacles. The objective for building the intelligent planner is to plan actions for multiple mobile robots to coordinate with others and to achieve the global goal while each agent achieves its local goal. Simulation results show that in order to control a large complex system, an intelligent planner can be designed for the CHA framework so that coordination among agents can be achieved.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".