Why AI Opponents Matter
Traditional solitaire practice is siloed. AI opponents replicate the tempo, risk tolerance, and mistake patterns of real rivals. You can rehearse counter strategies without scheduling scrims.
Benefits
- Exposes you to pressure scenarios on demand.
- Measures how quickly you adapt to opponent adjustments.
- Generates annotated replays that pinpoint decision drift.
Building a Baseline Model
Start with your own data. Feed historical runs into a machine learning notebook (Python or Swift Playgrounds) to train a baseline performance profile. This model acts as your mirror match.
Baseline workflow
- Export run data from Numbers as CSV.
- Normalize columns such as decision time, redeals, and error tags.
- Train a gradient boosted tree or simple neural network to predict move choices.
- Use the model within a simulator to generate alternate outcomes.
Creating Rival Personas
Once your baseline works, build opponent personas. Each persona tweaks aggression, risk, and speed. Label them after real rivals to make practice more immersive.
Sprinter
Prioritises speed over safety, ideal for turn one Klondike rehearsals.
Strategist
Prefers low variance moves, simulating finals opponents who rarely err.
Wild Card
Injects random pauses and gambles to train composure when chaos strikes.
Evaluating Simulation Quality
Good simulations feel human. Compare AI outputs to real match footage. Adjust parameters if the model makes impossible plays or misses obvious lines.
Quality checklist
- Match average decision time variance to human benchmarks.
- Ensure the AI respects game rules such as redeal limits.
- Review a sample of moves manually to confirm realism.
Integrating AI into Daily Practice
Schedule two to three AI sessions per week. Treat them like scrims: set goals, record results, and debrief. Alternate between persona types to avoid predictability.
Save transcripts in Freeform with annotations. Over time you will see precisely which opponent style trips you up and can adjust training accordingly.
Ethical Considerations
Keep AI practice fair. Do not use models trained on private opponent data without consent. Share your methodology openly so the community grows together.