USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS
Why this work is in the frame
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Bibliographic record
Abstract
Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions. In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.
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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 | 0.001 |
| Open science | 0.000 | 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 it