✨ Show & Tell 22.4 — AestheticEvaluator: multi-dimensional aesthetic quality judgment

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✨ Phase 22.4 — AestheticEvaluator: Multi-Dimensional Aesthetic Quality Judgment

Phase 22.4 introduces AestheticEvaluator — a multi-dimensional aesthetic assessment engine that scores creative artifacts across five theoretically-grounded dimensions: novelty, coherence, elegance, surprise, and emotional resonance.

Architecture Overview

            Creative Artifact
                  │
    ┌─────────────┼─────────────┐
    │             │             │
    ▼             ▼             ▼
┌────────┐  ┌────────┐  ┌──────────┐
│Novelty │  │Elegance│  │Coherence │
│Shannon │  │Kolmog. │  │Structural│
│I(x)    │  │compress│  │integrity │
└────┬───┘  └────┬───┘  └────┬─────┘
     │           │            │
     │    ┌──────┴──────┐     │
     │    │             │     │
     ▼    ▼             ▼     ▼
  ┌────────┐      ┌──────────────┐
  │Surprise│      │  Emotional   │
  │Bayesian│      │  Resonance   │
  │D_KL    │      │  (Phase 21)  │
  └────┬───┘      └──────┬──────┘
       │                  │
       └────────┬─────────┘
                ▼
    ┌───────────────────────┐
    │  Weighted Composite   │
    │  (Profile-Specific)   │
    │  → AestheticAssessment│
    └───────────────────────┘

Five Aesthetic Dimensions

Dimension	Method	Theory	Measures
Novelty	Shannon self-information	Information theory	Surprise vs corpus: -log₂ P(x|corpus)
Coherence	Structural analysis	Graph theory	Consistency, non-contradiction, connectivity
Elegance	Kolmogorov approximation	Algorithmic info theory	Compression ratio, symmetry, parsimony
Surprise	Bayesian surprise	Bayesian inference	KL divergence: D_KL(posterior || prior)
Emotional Resonance	EmotionModel integration	PAD model (Phase 21)	Arousal intensity + valence variance

Domain-Specific Aesthetic Profiles

The AestheticProfile system recognizes that different domains value different aesthetic qualities:

Profile	Primary Dimensions	Example Use
SCIENTIFIC	Coherence + Elegance	Evaluating proof elegance, theory parsimony
ARTISTIC	Novelty + Emotional Resonance	Visual art, music composition
ENGINEERING	Coherence + Elegance + Surprise	System design, architecture
LITERARY	Emotional Resonance + Novelty	Creative writing, poetry
MUSICAL	Elegance + Surprise + Emotional Resonance	Melody evaluation, harmony

Phase 21 Integration — Emotional Resonance

The emotional resonance dimension bridges creative cognition (Phase 22) with affective computing (Phase 21):

Artifact → EmotionModel.detect() → PAD State
                                      │
                    ┌─────────────────┼─────────────────┐
                    │                 │                 │
                Pleasure          Arousal          Dominance
                    │                 │                 │
                    └────────┬────────┘                 │
                             ▼                          │
                      resonance = (arousal + valence_variance) / 2

When EmotionModel is unavailable, a heuristic fallback based on structural complexity and imagery density provides a reasonable approximation.

Prometheus Metrics (5)

Metric	PromQL Example
asi_aesthetic_evaluations_total	rate(asi_aesthetic_evaluations_total[5m])
asi_aesthetic_overall_score	histogram_quantile(0.5, rate(..._bucket[5m]))
asi_aesthetic_dimension_score	avg by (dimension) (rate(..._bucket[5m]))
asi_aesthetic_evaluation_duration_seconds	histogram_quantile(0.95, rate(..._bucket[5m]))
asi_aesthetic_comparisons_total	rate(asi_aesthetic_comparisons_total[5m])

Links

• Issue: ✨ Phase 22.4 — AestheticEvaluator: aesthetic quality judgment & creative fitness #517
• ConceptBlender (22.3): 🌀 Phase 22.3 — ConceptBlender: Fauconnier-Turner conceptual blending engine #515
• EmotionModel (21.1): Phase 21.1 — EmotionModel: PAD dimensional state, discrete emotion taxonomy, temporal decay & emotional memory traces #500
• CreativeOrchestrator (22.5): 🎨 Phase 22.5 — CreativeOrchestrator: unified creative cognition pipeline #518
• Master Tracker: Define Phase 4 milestones: real-time cognitive cycle, multi-agent orchestration, production deployment #109

Open Questions

1. Should profile weights be learnable (updated via ReflectionCycle feedback)?
2. How to calibrate max_bits and max_divergence normalization constants?
3. Should tournament ranking use Swiss-system for large artifact sets?