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.

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.

Data Structures

struct SymbolicPattern {
    id: Uuid,
    name: String,
    trigger_tags: Vec<SymbolTagId>,
    forbidden_tags: Vec<SymbolTagId>,
    required_valence: Option<(SymbolTagId, f32)>,
    result: PatternEffect,
    priority: u8,
}

enum PatternEffect {
    MutateMemoryTags {
        from: SymbolTagId,
        to: SymbolTagId,
        condition: Option<MutationCondition>,
    },
    MutateBeliefTags {
        from: SymbolTagId,
        to: SymbolTagId,
        condition: Option<MutationCondition>,
    },
    InjectSymbolicEvent {
        event_type: String,
        associated_tags: Vec<SymbolTagId>,
    },
    FlagEidosAnchor(SymbolTagId),
}

struct MutationCondition {
    memory_origin: Option<MemoryOrigin>,
    min_valence: Option<f32>,
    memory_age_range: Option<(u64, u64)>,
}

Matching Pipeline

fn evaluate_patterns(agent: &Agent, memory: &[MemoryEvent], beliefs: &BeliefSet) -> Vec<PatternMatchResult>

• 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

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,
}

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.