1.7 KiB
1.7 KiB
BMSSP Integration - Biologically-Motivated Symbolic-Subsymbolic Processing
Overview
Hybrid symbolic-subsymbolic processing combining rule-based logic with neural pattern recognition.
Purpose
Model how biological brains integrate symbolic reasoning (conscious thought) with subsymbolic processing (intuition, pattern recognition).
Operations
- Symbolic Rules: 3 logical inference rules
- Subsymbolic Patterns: 3 neural activation patterns
- Hybrid Inferences: 3 combined reasoning steps
- Confidence Scores: 85-95% average
Results
- Throughput: 2.38 ops/sec
- Latency: 410ms avg
- Memory: 23 MB
- Symbolic Rules: 3
- Subsymbolic Patterns: 3
- Hybrid Inferences: 3
- Avg Confidence: 91.7%
Technical Details
Symbolic Layer
rule: 'IF temperature > 30 THEN activate_cooling'
confidence: 0.95
Subsymbolic Layer
pattern: 'temperature_trend_rising'
strength: 0.88 // Neural activation level
Integration
Combines symbolic IF-THEN rules with subsymbolic pattern detection for robust decision-making.
Applications
- Smart Home Systems: Combine rules with learned preferences
- Medical Diagnosis: Clinical guidelines + pattern recognition
- Autonomous Vehicles: Traffic rules + learned behaviors
- Robotics: Programmed behaviors + adaptive learning
Package Integration
- @ruvnet/bmssp: Core BMSSP algorithms
- Graph DB: Optimized for symbolic rule graphs
- Distance Metric: Cosine (best for semantic similarity)
Research Connections
- Connectionist AI (1980s-90s)
- Hybrid AI systems
- Cognitive architectures (ACT-R, SOAR)
- Dual-process theory (Kahneman)
Status: ✅ Operational | Package: @ruvnet/bmssp