Hunting Algorithm Visualization and Performance Evaluation Through BDI Agent Simulation
Bibliographic record
Abstract
This paper presents an agent-based simulation approach to evaluating hunting algorithm performance using Netlogo and the Belief, Desire, and Intent (BDI) architecture. Netlogo was customized to a number of two-dimensional scenarios with both hunting agents and prey agents interacting with each other and with the obstacles within the scenarios. The agents were developed with the flexibility to instantiate many types of hunters and prey. The differences in both the hunting agent and prey agents are with their skills (communications, perception, speed, etc.) and their cognitive abilities. To evaluate the viability of the approach, it was used to evaluate two hunting algorithms: the Lion Optimization Algorithm (LOA) and the Grey Wolf Optimization (GWO) algorithm. The experimental results show that the LOA was more resilient to obstacles than was the GWO. In the presence of obstacles, the lionesses are more reliable in completing joint convergence onto the prey. While the wolves have a lower convergence rate, they display an ability to recover from the confusion caused by obstacles to finally close in on their prey. The research concludes that the NetLogo programming environment is successfully adapted to the BDI architecture and is very effective in structuring a large agent-based system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".