cognitive/docs/templates/ai_concept_template.md

AI-Oriented Concept Template

---

title: Concept Name type: ai_concept status: draft created: YYYY-MM-DD updated: YYYY-MM-DD complexity: [basic|intermediate|advanced] processing_priority: 1-5 semantic_relations:

• type: prerequisite links: []
• type: implements links: []
• type: extends links: [] knowledge_graph: centrality: 0.0 density: 0.0 cluster_coefficient: 0.0 embeddings: model: sentence-transformers/all-mpnet-base-v2 vector: [] tags:
• concept
• category
• domain validation: completeness: 0.0 consistency: 0.0 coherence: 0.0

---

#BEGIN_CONCEPT_DEFINITION Brief, precise definition optimized for machine processing and semantic understanding. #END_CONCEPT_DEFINITION

Formal Specification

Mathematical Foundation

# Mathematical formalization of the concept

Logical Framework

% Logical representation
concept(X) :- property(X), relationship(X, Y).

Computational Model

class ConceptModel:
    """Computational representation of the concept"""
    def __init__(self):
        self.properties = {}
        self.relationships = []

Knowledge Integration

Ontological Position

ontology:
  is_a: []
  part_of: []
  related_to: []
  implements: []

Semantic Relationships

• Prerequisites: concept1, concept2
• Dependencies: dependency1, dependency2
• Applications: application1, application2

Context Embedding

context:
  domain: []
  scope: []
  constraints: []

Implementation Framework

Core Components

# @component_specification
{
    "interfaces": [],
    "behaviors": [],
    "constraints": []
}

Integration Points

# @integration_specification
{
    "inputs": [],
    "outputs": [],
    "protocols": []
}

Validation Criteria

# @validation_specification
{
    "invariants": [],
    "assertions": [],
    "tests": []
}

Machine Learning Aspects

Training Considerations

training:
  data_requirements:
    - type: structured
      source: [[data_source1]]
    - type: unstructured
      source: [[data_source2]]
  model_architecture:
    - type: neural_network
      specification: [[architecture_spec]]

Feature Space

# @feature_specification
{
    "dimensions": [],
    "representations": [],
    "transformations": []
}

Performance Metrics

# @performance_metrics
{
    "accuracy": 0.0,
    "precision": 0.0,
    "recall": 0.0,
    "f1_score": 0.0
}

Processing Instructions

Priority Levels

processing:
  critical_path: boolean
  dependency_order: integer
  parallel_processing: boolean

Resource Requirements

resources:
  memory: string
  computation: string
  time_complexity: string

Optimization Hints

# @optimization_hints
{
    "caching_strategy": "",
    "parallelization": "",
    "approximation": ""
}

Validation Framework

Consistency Checks

# @consistency_validation
def validate_concept():
    """
    Validation steps:
    1. Check internal consistency
    2. Verify relationships
    3. Validate implementations
    """
    pass

Completeness Metrics

completeness:
  definition: 0.0
  relationships: 0.0
  implementations: 0.0
  validations: 0.0

Quality Assurance

# @quality_metrics
{
    "formal_correctness": 0.0,
    "semantic_clarity": 0.0,
    "implementation_coverage": 0.0
}

Examples

Minimal Example

# @minimal_example
def demonstrate_concept():
    """Minimal working example of the concept"""
    pass

Complete Implementation

# @complete_example
class ConceptImplementation:
    """Full implementation with all features"""
    pass

Integration Example

# @integration_example
def integrate_with_system():
    """Example of system integration"""
    pass

Related Concepts

• concept1 {type: prerequisite, weight: 0.8}
• concept2 {type: implements, confidence: 0.9}
• concept3 {type: extends, similarity: 0.7}

References

• reference1
• reference2
• reference3

#BEGIN_METADATA_VALIDATION Last validated: YYYY-MM-DD Validation score: 0.0 Coverage: 0.0 #END_METADATA_VALIDATION