Symbolic Pattern Recognition for Efficient Belief and Memory Mutation

Purpose

To efficiently match symbolic patterns across memory and belief systems, triggering controlled mutations without introducing introspective recursion or memory bloat. This system supports emergent narrative evolution at scale while preserving determinism and simulation performance.

Introduction

Memetic Pattern Recognition is a core system utilized by both the Director AI and the Incarnation’s agent AI. It enables the identification, cataloging, and weighting of recurring symbols, phrases, and narrative structures within the player’s actions and choices. This document outlines the constraints and requirements for the system as implied by the Director AI design specification.

System Constraints

1. Valence Weighting

• Detected patterns must be weighted by their valence within both the Incarnation community and the player’s evolving narrative identity.
• Ambivalence acts as a suppressing factor, ensuring that the system does not enforce specific alignments but accommodates both conflicting and agreeing additions.

2. Dynamic Mapping

• The system must maintain a dynamic map of the player’s evolving narrative identity, cataloging explicit decisions and subtler patterns such as:
  • Artifact usage
  • Faction alliances
  • Recurring dilemmas

3. Thematic Adaptation

• Identified motifs must inform thematic adaptations, ensuring that new events feel like natural extensions of the ongoing narrative.
• The system must surface callbacks, echoes, and inversions of motifs to deepen continuity across different Tapestries.

4. Scalability

• The system must handle complex narrative patterns as the player’s journey evolves, ensuring performance and responsiveness.

5. Integration

• Seamless integration with the Director AI and the Incarnation’s agent AI is required to ensure consistent narrative resonance across all systems.

Design Principles

• No deep introspection: Agents do not recursively analyze their own thoughts; matching is shallow and local.
• Deterministic Evaluation: All matching is seed-deterministic based on local state and registered pattern corpus.
• Memory-Safe: Agents only track pointers to shared SymbolTags and compressed pattern results.
• Trigger-Driven: Mutation occurs when compact symbolic conditions are met, not via free association.
• Pattern Sharing: Common patterns are stored globally and indexed efficiently.

Matching Pipeline

• Pre-filter memory events using tag indices
• Check presence of all trigger_tags, absence of forbidden_tags
• Evaluate lightweight conditions (e.g., valence threshold, memory age)
• Resolve effect (single step, no cascading logic)
• Apply mutation or annotation

Performance Strategies

• Shared global pattern table indexed by trigger_tag
• Memory entries are fixed-size, pointer-based, symbolically encoded
• Beliefs compressed as SymbolTag digests (e.g., Bloom-style summarization)
• Matching is single-pass, batched, and non-recursive
• No per-agent pattern registration (patterns are global)
• Deterministic hashing for random-effect resolution

Integration Points

Notes

• No reflection or graph search at runtime; pattern matching is fully externalized and localized.
• Patterns may be prioritized or region-specific.
• Patterns evolve via generation rules, not agent reasoning.

This system enables large-scale symbolic evolution across the galaxy while preserving performance determinism and agent simplicity.

Example Patterns

SymbolicPattern {
    id: uuid!("0001-myth-echo"),
    name: "Heroic Sacrifice Echo",
    trigger_tags: vec!["sacrifice", "betrayal", "remembrance"],
    forbidden_tags: vec!["chaos"],
    required_valence: Some(("sacrifice", 0.5)),
    result: PatternEffect::InjectSymbolicEvent {
        event_type: "echo_resonance",
        associated_tags: vec!["cycle", "echo"],
    },
    priority: 10,
}

SymbolicPattern {
    id: uuid!("0002-fracture-mutation"),
    name: "Fracture Drift under Isolation",
    trigger_tags: vec!["fracture"],
    forbidden_tags: vec!["solidarity"],
    required_valence: Some(("fracture", -0.5)),
    result: PatternEffect::MutateBeliefTags {
        from: "fracture",
        to: "chaos",
        condition: Some(MutationCondition {
            memory_origin: Some(MemoryOrigin::DirectExperience),
            min_valence: Some(-0.5),
            memory_age_range: Some((200, 10_000)),
        }),
    },
    priority: 8,
}