AI Experience Management

Summary

AI experience management is the use of an AI system to monitor the player’s actions, infer something about their current experience, and then adjust the game so the experience stays within a desired range. In the interactive-narrative literature this is often called a drama manager or AI game master. The goal is not simply to win against the player, but to shape pacing, tension, challenge, surprise, or story progression in ways that improve the overall experience. (Bulitko et al., Game AI Pro 2, see source-game-ai-pro-2; Yannakakis and Togelius, Artificial Intelligence and Games, see source-ai-and-games)

Key ideas

• Player model first: The system needs some estimate of what the player is doing or feeling: skill, frustration, curiosity, preferred play style, or narrative appetite.
• Interventions second: Once the system has a model, it chooses an intervention: spawn different content, delay a story beat, move resources, alter encounter pressure, or surface a different branch.
• Experience targets: The adaptation is guided by target variables such as dramatic tension, challenge, pacing, or variety rather than by one mechanical metric alone.
• Local vs global adaptation: Some interventions are small and immediate (change the next encounter); others are long-horizon (change the campaign’s structure or future plot content).
• Authorial limits matter: Experience management works best when the set of allowed interventions is explicit. If the AI can change everything, it becomes opaque and difficult to debug.

In practice

A simple experience-management loop looks like this:

1. Record telemetry and current story/game state.
2. Update a compact player model.
3. Score the available interventions against the desired experience target.
4. Apply the best-scoring intervention that does not violate design constraints.

Unity/C# sketch:

public class ExperienceManager : MonoBehaviour
{
    [SerializeField] private float targetTension = 0.65f;
    [SerializeField] private EncounterDirector director;
 
    private PlayerModel model = new();
 
    public void RecordCombatOutcome(float damageTaken, float completionTime)
    {
        model.UpdateFromCombat(damageTaken, completionTime);
    }
 
    public void EvaluateNextBeat()
    {
        float tensionGap = targetTension - model.CurrentTension;
 
        if (tensionGap > 0.2f)
            director.SchedulePressureBeat();
        else if (tensionGap < -0.2f)
            director.ScheduleRecoveryBeat();
        else
            director.ScheduleNeutralBeat();
    }
}

This is deliberately small-scale, but it captures the core idea: maintain a player-facing target state and select future content accordingly.

Evidence

• Bulitko and colleagues describe the AI game master as a bundled system that monitors player actions and modifies the story dynamically rather than treating the narrative as fully fixed. (Game AI Pro 2, see source-game-ai-pro-2)
• Artificial Intelligence and Games places experience management alongside player-modelling and procedural-content-generation-ai: the system needs a model of the player and a space of possible content adjustments in order to work.
• The approach is strongest in relatively constrained games because the intervention space is smaller and easier to author. Open worlds make the state space much harder to manage cleanly.

Implications

• Experience management connects programming/AI directly to narrative-design, player-modelling, game-analytics, and flow.
• It is best thought of as curation, not omniscient control. The AI should shape probabilities and sequencing, not completely replace authored structure.
• CRE342 adds a psychological warning: players often accept adaptation when it feels responsive and fair, but not when it feels opaque or rigged. That makes readability and trust part of the implementation problem, not just tuning details. (CRE342 Lectures, see source-cre342-lectures)
• The ethical boundary matters. A system that adapts to improve readability or maintain flow is different from a system that adapts purely to maximise monetisation or retention at any cost.

Open questions

• How far can experience management scale beyond relatively linear or hub-based games into large open worlds?
• What is the smallest useful player model for a student project: skill only, or skill plus preference plus frustration?
• Which interventions are most legible to players without making the adaptation feel artificial?

Related

• source-cre342-lectures

player-modelling · game-analytics · procedural-content-generation-ai · narrative-design · flow · psychology-of-adaptive-play · overview-artificial-intelligence-in-games · source-game-ai-pro-2