REI's Cognitive Layers
Last updated
Last updated
REI's cognitive architecture represents a sophisticated interplay between four distinct layers, each interacting with the Oracle System and ERCData in unique ways. This layered approach enables complex analysis while maintaining deterministic blockchain interactions.
The Thinking Layer serves as REI's primary interface with raw data. Like the analytical left brain in humans, it processes concrete information and identifies base patterns.
The Thinking Layer primarily uses the Oracle System for:
Raw data retrieval
Initial pattern matching
Metric calculation
Event processing
With ERCData, the Thinking Layer:
Queries existing patterns
Stores new observations
Updates basic metrics
Maintains data relationships
The Reasoning Layer adds context and meaning to processed information. It understands relationships, implications, and broader patterns.
The Reasoning Layer leverages the Oracle for:
Complex pattern recognition
Historical analysis
Relationship discovery
Context verification
With ERCData, it performs:
Pattern relationship mapping
Context storage
Historical analysis
Relationship indexing
The Decision Layer synthesizes information from previous layers to determine appropriate actions.
The Decision Layer uses the Oracle to:
Verify potential actions
Check state consistency
Validate patterns
Confirm feasibility
With ERCData, it:
Checks historical decisions
Stores decision patterns
Updates action records
Maintains decision context
The Acting Layer transforms decisions into concrete blockchain actions.
The Acting Layer relies on the Oracle for:
Action transformation
State verification
Execution monitoring
Result confirmation
With ERCData, it handles:
Action recording
State updates
Pattern confirmation
Result storage
The true power of REI's architecture emerges in how these layers work together:
This interaction flow enables:
Comprehensive data analysis
Context-aware processing
Intelligent decision making
Reliable execution
Throughout these layers, memory systems maintain context and enable learning:
The memory systems ensure:
Information persistence
Pattern learning
Context maintenance
State consistency
The final output from this layered processing is always deterministic, though the path to that output may involve complex analysis:
This sophisticated yet deterministic processing enables REI to provide intelligent insights while maintaining the reliability requirements of blockchain systems. In the next section, we'll explore how this architecture enables REI's social media interactions and real-time processing capabilities.
