SimMind
A floating island world of glass-barked trees, with a figure standing on a path

Adaptive offers for LiveOps teams

Find the offer opportunities your current setup misses

SimMind turns gameplay context into offer strategies and recommends updates as players’ needs change.

01 The problem

Your offer logic needs gameplay context.

Connect player signals to game rules, available actions, and changing needs.

  1. Unrecognized needs. Missed opportunities.

    A failed level tells you what happened. Remaining objectives and available resources reveal where an offer could help.

  2. Changed situations. Outdated offers.

    A new reward or completed goal can remove the reason for an offer. The strategy needs to reflect that change.

02 Adaptive offers

Understand the situation.Keep the strategy relevant.

Adaptive offers need a model of what the player can do next—and when the reasons behind an offer no longer hold.

Product preview

FIG.01 Illustrative example — not live data

  1. Player evidence

    Three failures. What’s stopping progress?

    Recent attempts
    Failed Level 47 ×3
    Last two attempts
    One target left, no moves remaining
    Available boosters
    None

    01

    Behavior alone doesn’t explain the need.

    Repeated failure is a signal. The remaining objective, available moves, and game rules explain what it means.

  2. Situation model

    Extra moves may help finish the level.

    Goal
    Clear Level 47
    Constraint
    No moves left
    Game rule
    Extra moves continue the current board
    Unknown
    Whether extra moves will be enough

    02

    Match the offer to a current player need.

    Connect the offer to a specific gameplay option. Keep uncertainty visible when the evidence is incomplete.

  3. Strategy to test

    Test an extra-moves offer.

    When
    A failed attempt ends near completion
    Why
    Continue the board instead of restarting
    Reassess
    Level cleared, boosters gained, or repeated offer declines

    For your review

    03

    When the situation changes, revisit the strategy.

    New resources, completed goals, and player responses can change whether the original offer still makes sense.

03 The agent

Keep offers relevant as players change.

Find missed opportunities, explain why an offer fits, and revise the strategy when that reason no longer holds.

Your team sets the goals, limits, and approvals.

Continuous reassessment

FIG.02 Agent loop · In development

Team goals · constraints · approval · economy rules

  1. 01

    Player situation

    What the player is trying to do, and what is in the way.

  2. 02

    Find opportunity

    Where could an offer actually help right now?

  3. 03

    Propose strategy

    Propose when to offer, why it fits, and when to stop.

  4. 04

    Apply constraints

    Frequency, eligibility, economy rules and approval.

  5. 05

    Act / recommend

    Hand the proposal to your team, or act inside agreed limits.

  6. 06

    Observe outcome

    What happened after the decision — including nothing.

  7. 07

    Reassess

    Check whether changing player needs call for keeping, revising, or stopping the strategy.

  8. Does the original reason still hold?

    If it doesn’t, the situation is read again and the strategy changes with it.

Evidence Every decision carries the observations it rests on, so it can be inspected and questioned.

04 Player situation

Same signal. Different decisions.

SimMind connects player goals, resources, and game rules to explain when an offer is relevant.

Observed data

Facts from telemetry

  • Level
  • Inventory
  • Spend
  • Activity
  • Resources
  • Recent events

Player situation

What these facts mean for the next decision.

Goal
What is the player trying to accomplish?
Obstacle
What is preventing progress?
Available means
What can the player actually do right now?
Game rules
Which actions and resources matter here?
Recent choices
What has the player already tried?
Evidence & unknowns
What do we know, what are we inferring, what is still missing?
  1. 01 Player data
  2. 02 Situation model
  3. 03 Decision
FIG.03 Illustrative example

Same observed signal

Failed Level 47 × 3

Game rule Only hammers remove armor.

Situation A

Goal
Finish the level
Blocker
Armored tile
Boosters
4 color bombs

Consider a hammer offer

Situation B

Goal
Finish the level
Blocker
Armored tile
Boosters
4 hammers

Use existing hammers first

05 Research Ongoing

Toward Adaptive Games

Game decisions · Player responses · Adaptive experiences

Explore research

06 Adaptive games

Offers first. Toward adaptive games.

We start with offers and explore how the same decision layer could adapt hints, difficulty, progression, and NPC behavior.

FIG.04 Current focus: offers · Exploring broader game decisions

Player situation

Shared decision layer

  • Offers Starting point
  • Hints
  • Difficulty
  • Progression
  • NPC behavior