Bibliographic record
Abstract
The field of AI has been a topic of interest for the better part of a century, where the goal is to have computers mimic human behaviour.Researchers have attempted to incorporate AI in different problem domains, such as autonomous driving, playing games like Chess and Go, diagnosis and security.They have also worked extensively on different subfields of AI such as machine learning, pattern recognition and voice operated-systems.This thesis concentrates on a subfield of AI which is the field of Learning Automata (LA).Rather than working with the well-established mathematical formulations of the field, our intention has been to use these tools to tackle a specific set of problems referred to as Elevator-Like Problems.Our work in this thesis considers a problem that has not been tackled before using AI.It involves the problem of optimizing the scheduling of elevators.In particular, we are concerned with determining the Elevators' optimal "parking" locations.Problems with similar characteristics are referred to as Elevator-like Problems (ELPs).In our case, the objective is to find the optimal parking floors for the set of available elevators so as to minimize the passengers' Average Waiting Time (AWT).Apart from proposing benchmark solutions, we have provided two different novel LA-based solutions for two different general settings, namely the single-elevator and the multi-elevator scenarios.The first pair of solutions are based on the well-known L RI scheme, and the second pair incorporate the Pursuit concept to improve the performance and the convergence speed of the first solutions, leading to the P L RI scheme.The simulation results presented demonstrate that our solutions performed better than those used in modern-day elevators, and provided results that are nearoptimal, yielding a performance increase of up to 90%.iii Chapter 4 presents the case of the multi-elevator building settings.Again, the chapter presents the solutions that we have used as benchmarks, which are MEP1 that extends SEP1, and MEP2 that extends SEP2 for the multi-elevator domain.The chapter then submits our extended LA-based solutions for the MEP, which we refer to as MEP3 and MEP4.We also include results that show how they performed, and compare them to the benchmark solutions.Chapter 5 concludes the thesis.It presents a summary of each chapter and discusses potential future work, namely, solving the ELP in non-stationary Environments, and the task of combining different dispatching policies in such settings.The results presented in the bodies of Chapters 3 and 4 are representative of the entire set of results that we have obtained.In the interest of readability, the complete suite of results are presented in Appendix A and Appendix B respectively.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".