Torrent Gear

How AI personalizes missions and tournament challenges

1) Why personalize

AI-personalization of missions and tournament tasks:

increases relevance (missions "in good shape," without a boring grind);
reduces frustration (difficulty and duration for the player's profile);
improves retention and engagement (visible progress, understandable goals);
protects the economy (controlled award issuance and honesty of conditions).

The key: a balance of personalization and fairness - individual goals should not give mathematical advantage in games.

2) Data signals (model inputs)

Behavioral: slot genres/providers, average rate, spin pace, session length, time of day, entrance frequency.

Progress: levels/XP, completion of past missions, success/failure in tournaments, streak 'and.

Financial: deposits/withdrawals (aggregated, no sensitive details), sensitivity to bonuses.

Social: participation in chats/events, clips/replays, community reactions (if any).

Context: device, input channel, geo-restrictions on content/providers.

RG signals: time/deposit limits, tendency to long sessions - to reduce complexity and soft pauses.

💡 Important: all models work with aggregated, anonymized features, without using PII beyond what is required by compliance.

3) Model stack

1. Clustering (unsupervised)

K-Means/DBSCAN/HDBSCAN → behavioral segments: "sprinter," "collector," "tournament starter," "brand-lay to providers."

Usage: select the basic "frame" of missions for the segment.

2. Propensity scoring (supervised)

Goal: Probability to complete X mission in T window, probability to participate/finish in tournament.

Models: Gradient Boosting (GBDT), logistic regression, tabular Transformers.

3. Contextual bandits

Purpose: online selection of mission type and complexity under context with exploration/exploitation control.

Methods: LinUCB/Thompson Sampling.

4. RL/Policy Learning (optional)

Goal: Optimize mission/task sequences (chains) to hold the player without overheating.

Restrictions: strict safety restrictions (see § 7).

4) Pipeline data and solution in sales

Collection of events: event bus (Kafka/Redpanda), schemes: spin, session_start/end, mission_progress, tournament_result.

Fichering: 1h/24h/7d frames; aggregates (median rate, pace variance, variety of providers).

Fitting/updating models: offline once every 1-7 days; online scoring at each session + partial additional training of the bandit.

Issuance restrictions: honesty policy (rate-limits, award caps, RG-restrictions).

Decision logging: who/when/which policy option is shown, chance, expected complexity, actual outcome.

5) Mission generator (decision logic)

1. Segment: cluster → basic mission basket (genres, duration).

2. Compliance filters: providers, geo, RG restrictions (including daily time limits).

3. Propensity scoring: ranking candidates by probability of completion and expected value (EV Retensna).

4. Contextual bandit: selection of 1-2 best candidates with the ε -exploration.

5. Difficulty tuning: adapting targets (number of spins/bet/time) to a peripheral window (e.g. weeknight/weekend).

6. Emission Cap: Seasonal Token/Cosmetics Budget Check.

7. A meaningful alternative: offer 1 spare mission ("change" button once every X hours).

6) Personalization of tournament tasks

The choice of league/division by MMR and history is independent of VIP (see the previous article).

Individual micro-goals within the tournament: "play 3 providers," "keep the pace ≤N spins/min," "badge for the top X%" - twist on propensities.

Flexible participation windows: time slots when the player is more often online; AI recommends a screening session.

Award tracks by profile: cosmetics and tokens taking into account rarities, but without increasing RTP/property.

7) AI Integrity Rules, Responsibilities and Limitations

Safety constraints: maximum N personal missions per day; prohibition of increasing complexity at RG fatigue signals.

Transparency: "How missions are selected" screen: segments, context, protection against failures (pity timers), caps of awards.

Fairness: same awards ceiling for everyone; personalization changes the path rather than the resulting value.

Responsible Gaming: soft pauses, "rest" recommendations, daily limits - built into policies.

Privacy: aggregates only; no PII in model features beyond the regulatory minimum.

8) Anti-abuse and anti-gaming

Detection of uniform cycles: repetitions with high frequency of mission → require variability (provider/bet/time).

Pace cap: no more than X missions/day, cooldown between "fast" tasks.

Difficulty-Guards: Lower/Upper Limits; sharp jumps are prohibited.

Tournament Collusions: Network/Behavioral Signatures, Random KYC Checks in Master Leagues.

Log audit: explainability of decisions (reason codes: segment, propensities, bandit-arm).

9) Success metrics

Uplift D7/D30 in personalized versus basic.

Mission Completion Rate and Median Time-to-Complete (TTC).

Stickiness (DAU/MAU), Avg Session Length (with RG guards).

Gini distribution of rewards (evenness with similar efforts).

Complaint Rate by "injustice" and Mute/Opt-out Rate personalization.

Prize ROI/Emission to GGR - Sustainability of the Promotional Economy.

Exploration Cost bandit and Regret - to set up ε/Thompson Sampling.

10) A/B patterns to run

1. Mission types: provider-specific vs genre.

2. Mission length: short (≤15 min) vs medium (30-40 min).

3. Pity timers: hard vs soft at the same p₀.

4. Bandit algorithm: LinUCB vs Thompson; different ε.

5. Change of mission: access 1/day vs 2/day.

6. Tournament micro-goals: one vs two parallel.

11) Templates (JSON) missions and tournament tasks

Mission (personalized):

json
{
"mission_id": "m. s3. var. playtime. diverse. 001," "title": "Open three worlds," "segment_hint": "collector," "difficulty": "medium," "requirements": [
{"type":"provider_diversity","providers":3,"window_min":30},   {"type":"bet_range","min":0. 2,"max":1. 0}
],  "pity": {"soft_delta":0. 02,"cap":0. 4,"hard_after_attempts":30},  "rewards": {"tokens": 12, "cosmetic_drop": {"rarity":"Rare","p":0. 12}},  "caps": {"daily_user_missions": 3, "economy_token_cap": 150}
}

Tournament micro-goal:

json
{
"task_id": "t. s3. qualifier. pacing. tempo",  "context": {"league":"Gold","time_slot":"evening"},  "goal": {"type":"pace_control","max_spins_per_min":45,"duration_min":20},  "vip_neutral": true,  "rewards": {"season_points": 120},  "fairness": {"max_value_equivalence": true}
}

12) Production pseudocode (contextual bandit)

python context: segment, time, device, recent TTC, RG flags context = build_context (user_id)

candidates = fetch_candidate_missions(segment=context. segment)
candidates = compliance_filter(candidates, context. geo, context. rg)

scored = [(m, propensity_score(m, context)) for m in candidates]
topK = top_by_score(scored, k=5)

the bandit chooses the "hand" (arm)
chosen = contextual_bandit. choose_arm(topK, context)

let's tune the complexity + check the emission budget personalized = adjust_difficulty (chosen, context)
if not economy_budget_ok(personalized):
personalized = degrade_reward(personalized)

log_decision(user_id, context, personalized)
deliver(personalized)

13) UX patterns

Transparency: "Matched to your style: 30-40 min, 3 providers, victory - a rare cosmetic drop."

Control: button "Change mission" (cooldown), toggle switch "disable personalization."

Smoothness: difficulty indicators, time score, progress bar with TTC forecast.

Quiet VFX: Short Animations of Success; feedback to failure - + fragments/pity progress.

14) Release plan

1. MVP (3-5 weeks): clustering + propensities for missions; static tournament problems; emission caps; transparency screen.

2. v0. 9: contextual thug; mission change; micro-goals in tournaments; full RG Guards.

3. v1. 0: RL mission chains; social goals; visual collections; "honesty" reports and log audits.

4. Next: seasonal template rotation, retro cosmetics comebacks, cross-promos with providers.

15) Pre-start checklist

Personalization does not affect RTP/math advantage.
Emission caps and daily mission limits.
Pity timers and deterministic milestones are set up.
How it Works screen + reason codes.
RG policies: pauses, limits, "disable personalization" option.
Anti-abuse: variability of requirements, pace cap, log audit of decisions.
Plan A/B and a list of target KPIs with success thresholds.

AI personalization is not "more difficult," but smarter: missions and tournament tasks adapt to the player's style, but remain honest and safe, emissions are in the budget, and the rules are transparent. Clustering + propensities provide the basis, contextual bandits optimize the display, RL improves the chains - and all this works only with clear constraints, RG guards and intelligible communication "how exactly we select targets."