Match setup
Learning controls
Adaptive arena
Opponent intelligence
Live match analysis
Decision log
Performance summary
Plotly analytics dashboard
Match history and data
| # | Result | Mode | Personality | Moves | Accuracy | Duration | Style |
|---|---|---|---|---|---|---|---|
| No completed matches yet. | |||||||
Challenges and achievements
How the adaptive opponent learns
Adaptive artificial intelligence
The opponent observes your choices, estimates your style, predicts likely actions, and changes counter-strategies as new evidence arrives.
Frequency and Markov prediction
Frequency learning studies common moves. Markov prediction studies which move usually follows your previous move.
Q-learning
Q-learning stores action values for simplified game states. Rewards strengthen useful responses while penalties reduce weak choices.
Exploration and exploitation
Exploration tries unexpected actions. Exploitation chooses the best-known response. The exploration slider controls that balance.
Dynamic difficulty adjustment
Adaptive difficulty studies win rate, streaks, move diversity, prediction difficulty, and recent improvement before tuning opponent strength.
Action relationship
Actions follow a circular strategy system. Each action defeats four nearby choices, loses to four others, and ties with itself or its opposite.
Generated on 2026-08-10. This simulation is educational and uses lightweight browser-based learning.