Evolving Players for a Real-Time Strategy Game Using Gene Expression Programming
Bibliographic record
Abstract
This thesis focuses on the fields of real-time strategy games, evolutionary computation, distributed machine learning and multi-agent systems. In general, the problem is to automatically learn the best strategy to play a real time strategy game, more precisely-a two-player combat of marines and tanks. The idea was inspired by ORTS RTS Game AI Competition held annually at University of Alberta. The given problem is very complex and multicriterial, thus final solutions presented here are the result of a constant development and countless improvements. In the paper we try to underline the iterative nature of this process and propose a methodology that could be used for different problems in the real-time games field. We show how to model the strategy as a multi-agent system and how to fine-tune the evolutionary process of searching best players. We also explore the subject of distributed learning, focusing on using a computation cluster for evaluating solutions. The methods of evaluation are also elaborated in the context of co-evolution, we compare two different methods that use competitive fitness- single elimination tournament and hall of fame. In order to
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".