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