Torrent Gear

AI algorithms to adapt game complexity

When and what exactly to adapt

Tempo and load: speed is flat, event frequency, number of opponents, wave timings.

Tactical complexity: accuracy of bots, their tactics, "cleverness" of the path.

Puzzles and hints: time window, number of steps, presence of "hints."

Resources and economy: loot, khil, checkpoints, timeouts.

Interface and accessibility: auto-sight, contrast, large fonts, "motion-free mode."

️ Gambling: you cannot change RTP/probabilities/paytables/character weights - only the presentation, animation pace, training tips, content showcases and RG numbers are adapted.

Signals: what AI understands' pain level'

Online signals

Segment time, number of retrays, deaths, damage/min, accuracy.

Behavioral patterns: sharp "quits," pauses, switching to light mode.

Biometrics/paralinguistics (if the player explicitly allowed): speech/breathing rate, micropause.

Device/network telemetry: fps drops, lags → complexity ≠ hardware.

Offline/Profile

Success history by genre/mode, training levels, calibration test results.

Accessibility settings (contrast, TTS, auto-sight) - respect the default selection.

Models and algorithms

1) Feedback controllers (quick start)

PID controller: target is average "voltage level" (e.g. 60-70% success rate).

Input: error = target − current success (or TTK/retire-rate).

Output: step of parameters change (speed is equal, AI accuracy).

Pros: simplicity, predictability. Cons: manual tuning required, local optima.

2) Contextual bandits ("here and now" adaptation)

LinUCB/Thompson Sampling with context: skill, device, fps, segment type.

An action (a set of complexity parameters) is selected, maximizing the "reward" (hold/flow-score) taking into account uncertainty.

Pros: form online training without heavy infrastructure, quickly converge.

3) Bayesian skill models

TrueSkill/Glicko-like updates to player rating and "segment rating."

Short and long skill dynamics are sutured, confidence intervals are given.

Useful for matchmaking and basic preconfiguration of difficulty before entering the level.

4) Sequences and prediction (RNN/Transformer)

Probability of frustration/quit at horizon N minutes is predicted.

Input: sequences of attempts, damage, errors, micro events UI.

Exit: "risk of overheating" → mild intervention (hint, checkpoint, pause).

5) RL directing (for large productions)

Renewal Learning as a "content director": the agent selects wave/puzzle configurations.

Rewards: Time in flow, reduced retraces, retention, respect for RG/availability.

Simulators/synthetic players and hard gardrails are required so as not to "train" manipulation.

Policies and gardrails (ethics by default)

Hard parameter boundaries: min/max for bot accuracy, speed, number of enemies.

Smoothness of changes: not more than X% shift in Y seconds; avoid "swings."

Transparency and control: the player can fix the difficulty, disable DDA, enable "story mode."

Accessibility> challenge: accessibility options are always stronger than automatic complexity.

Gambling: no adaptation of odds/payoffs; only training prompts, tempo, and RG interventions.

Anti-exploit: protection against "sandbagging" (artificially understating the skill for bonuses).

UX patterns of "careful" adaptation

Micro tales after N failures: "Press ⓘ for hint (no fines)."

Soft pause: "It looks like the segment is more difficult than usual. Simplify timings? [Yeah, no".

Calibration level: 1-2 minutes of practice with quick determination of the initial profile.

Complexity control center: widget with the current level, history of changes, the option "return as it was."

Communication without stigma: Avoid "You're too weak." Better: "Let's pick up a comfortable pace."

Success Metrics (KPIs)

Flow/success: average% of segments passing in ≤K attempts; the average time between "mini-victories."

Retray/quit: decrease in rage-quit, decrease in repeats over the threshold.

Hold and sessions: DAU/WAU, intermittent time, return to complex segments.

Availability: share of players who included assist options; CSAT by availability.

Model stability: the number of "retrains," the magnitude and frequency of adjustments.

Trust: complaints about "twisting," clicks on "why adapted."

Implementation Architecture (Outline)

1. Telemetry: battle/puzzle events, retrays, damage, accuracy, fps, pauses; normalization and anonymization.

2. Feature Store: rolling aggregates by player and segment; device/network features.

3. Inference layer: bandit/bayes/controllers; SLA <50-100 ms.

4. Policy Engine: limits, smoothness, prohibitions (especially for gambling).

5. Orchestration: applying parameters, hints, checkpoints, pauses.

6. Observability: online dashboards of metrics, drift alerts, A/B experiments.

7. Privacy and security: PII minimization, on-device inference for sensitive, encryption of logs.

Evaluation process: A/B and online calibration

A/B/C: fixed complexity vs PID vs bandit; target metrics - flow-rate, quits, satisfaction.

Sensitivity analysis: how KPIs respond to parameter boundaries.

Calibration by cohort: device, experience, mode (campaign/live), availability.

Common mistakes and how to avoid them

Difficulty saw: too aggressive steps → add inertia/hysteresis.

Not counting iron: the fall of fps is "masked" as a growth of skill → separate the performance from the skill.

Manipulating the reward: delaying a victory for the sake of retention is a blow to trust.

Stealth: lack of explainability and manual control → complaints of "twisting."

Gambling: any impact on probability - legal/ethical risk.

Roadmap 2025-2030

2025-2026 - Base

Telemetry, PID controllers for pace, difficulty control center, A/B on bandits, explanations for the player.

2026-2027 - Skill Models

Bayesian skill (TrueSkill-like), prediction of frustration (Transformer), personal "help windows."

2027-2028 - RL Directing

Simulators, secure policies, RL agent for wave/puzzle configurations; he-device assist model.

2028-2029 - Composability and Availability

DDA plugins for level editor, auto-accessibility checks, public ethics reports.

2030 - Industry Standard

Certified gardrails, general format of explainable logs, "DDA-by-default" with visible player control.

Pilot checklist (30-60 days)

1. Define the target flow corridor (for example, 60-70% segment success).

2. Turn on the telemetry of key signals and separate the performance factors (fps/lag).

3. Start the PID controller on 1-2 parameters (tempo, timing window) with soft borders.

4. In parallel - a contextual bandit for choosing complexity presets.

5. Add UX control: mode switch, prompts, "why has changed."

6. Conduct A/B, measure flow, quits, CSAT, inclusion of assist options.

7. Start policy-gardrails (and for gambling modes - prohibitions on changing probabilities).

8. Iterations weekly: tuning borders, improving explainability, expanding to new segments.

Mini cases (what it looks like)

Shooter: after 3 deaths on the checkpoint - the accuracy of enemies decreases by 6% and less often grenades; viewline tooltip.

Puzzle: after 120 seconds of stagnation - "sparks" around the activated elements; riddle timer + 10%.

Runner: if fps sags, the speed of the environment temporarily decreases, but the hitboxes do not change.

Slot-like (entertaining, not gambling): animations between the backs are accelerated, training tips appear; the winning math doesn't change.

AI adaptation of complexity is about respect for the player: keep him in the stream, help overcome obstacles and give freedom of choice. Technically, it relies on clear signals, transparent algorithms and hard gardrails. In gambling scenarios - even more so - there is no effect on the probability of winning: only pace, serving and concern for well-being. This is how games are built that you want to return to - because they are honest, accessible and truly exciting.