Bibliographic record
Abstract
This thesis focuses on investigating searching algorithms which can solve the Ants Nearby Treasure Search (ANTS) Problem in the presence of obstacles.In the ANTS problem, there are k ants (modelled as mobile agents) initially located at the origin in a two-dimensional grid.Pheromones are used as physical markers to allow the ants to perform collaborative search.The target treasure is located at an unknown location at distance D from the origin.This thesis studied ant foraging in the systems with and without obstacles.A simple deterministic foraging algorithm is provided first, which uses synchronous identical ants for the environment without obstacle.This simple foraging algorithm is a spiral searching algorithm with comparable complexity to the main algorithm of C. Lenzen and T. Radeva [1].One significant improvement is that the ants do not need to know the direction in which the nest lies.An extension of this algorithm using an additional marker can achieve a global termination and can allow all the ants to find the target.Further, two additional types of algorithms for ant foraging in a N × N square terrain are introduced.The two types algorithms target different models which contain obstacles in different categories.The Zig-Zag foraging algorithm and the Up-Down foraging algorithm solve the searching problem using a single ant in a bounded environment with large obstacles.It is shown that the ant is able to successfully explore the entire terrain with pheromones provided that the passages between obstacles have width at least five cells.For the second type, the spiral searching algorithms work in the model with k ants in a wrap-around environment having randomly placed single cell obstacles.All of our algorithms are implemented in Netlogo and the corresponding simulations and detail explanations are presented by using examples.I would like to express my sincere gratitude to my supervisor Dr. Evangelos Kranakis for his guidance, patience, and unrelenting support throughout my study.I have been extremely lucky to have him as my supervisor who cared so much about my work, who responded to all my questions, and who encouraged me and guided me at all times.I would also like to thank Dr. Stefan Dobrev who contributed to the idea of the Up-down foraging in Chapter 5 after our discussions.Special thanks to Steven Porretta
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".