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cognitive/docs/concepts/theoretical_foundations.md
Daniel Ari Friedman 6caa1a7cb1 Update
2025-02-07 08:16:25 -08:00

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Theoretical Foundations

title: Theoretical Foundations type: concept status: stable created: 2024-02-06 tags:

Overview

This document outlines the theoretical foundations that underpin our cognitive modeling framework's documentation system, integrating principles from cognitive science, knowledge representation, and machine learning. For comprehensive cognitive science theory, see the knowledge_base/cognitive/cognitive_science.

Core Principles

1. Knowledge Representation

# @knowledge_structure
knowledge_model = {
    "hierarchical": {
        "concepts": ["[[knowledge_base/cognitive/cognitive_phenomena|Cognitive Phenomena]]", "[[theoretical_foundations]]"],
        "implementations": ["[[knowledge_base/cognitive/active_inference|Active Inference]]", "[[belief_updating]]"],
        "validations": ["[[validation_framework]]", "[[testing_guide]]"]
    },
    "relational": {
        "bidirectional": ["[[linking_completeness]]", "[[linking_patterns]]"],
        "semantic": ["[[ai_semantic_processing]]", "[[machine_readability]]"],
        "temporal": ["[[version_control]]", "[[changelog]]"]
    }
}

2. Cognitive Architecture

See knowledge_base/cognitive/cognitive_phenomena for detailed theory.

Active Inference Framework

Belief Systems

Action Selection

Documentation Integration

1. Machine Readability

See machine_readability for implementation details.

# @documentation_structure
doc_structure = {
    "semantic_markup": {
        "concepts": "[[concept_template]]",
        "implementations": "[[implementation_template]]",
        "relationships": "[[relationship_template]]"
    },
    "validation_rules": {
        "completeness": "[[validation_framework]]",
        "consistency": "[[linking_validation]]",
        "quality": "[[quality_metrics]]"
    }
}

2. Knowledge Organization

See knowledge_organization for detailed patterns.

Hierarchical Structure

Network Structure

3. Version Management

See version_control for implementation.

# @version_management
version_structure = {
    "documentation": {
        "current": "[[api_reference]]",
        "history": "[[changelog]]",
        "migrations": "[[migration_guide]]"
    },
    "code": {
        "releases": "[[release_management]]",
        "branches": "[[branching_strategy]]",
        "tags": "[[version_tags]]"
    }
}

Implementation Architecture

1. Documentation System

# @doc_system
class DocumentationSystem:
    """
    Core documentation system architecture.
    See [[documentation_standards]] for guidelines.
    """
    def __init__(self):
        self.knowledge_base = KnowledgeGraph()
        self.validator = ValidationFramework()
        self.processor = SemanticProcessor()
    
    def process_document(self, doc: Document) -> ValidationResult:
        """
        Process and validate documentation.
        See [[ai_validation_framework]] for details.
        """
        # Implementation
        pass

2. Knowledge Graph

# @knowledge_graph
class KnowledgeGraph:
    """
    Knowledge graph implementation.
    See [[knowledge_organization]] for structure.
    """
    def __init__(self):
        self.nodes = {}  # Concept nodes
        self.edges = {}  # Relationships
        self.metadata = {}  # Node metadata
    
    def add_relationship(self, source: Node, target: Node, type: str):
        """
        Add semantic relationship.
        See [[linking_patterns]] for valid types.
        """
        # Implementation
        pass

3. Validation System

# @validation_system
class ValidationSystem:
    """
    Documentation validation system.
    See [[validation_framework]] for rules.
    """
    def __init__(self):
        self.rules = self.load_rules()
        self.metrics = QualityMetrics()
    
    def validate_document(self, doc: Document) -> ValidationResult:
        """
        Validate documentation against rules.
        See [[quality_metrics]] for criteria.
        """
        # Implementation
        pass

Theoretical Integration

1. Cognitive-Documentation Mapping

graph TD
    A[Cognitive Architecture] --> B[Documentation Structure]
    B --> C[Knowledge Graph]
    C --> D[Validation System]
    
    E[Active Inference] --> F[Information Processing]
    F --> G[Knowledge Organization]
    G --> H[Quality Metrics]

2. Processing Pipeline

graph LR
    A[Document Input] --> B[Semantic Processing]
    B --> C[Knowledge Integration]
    C --> D[Validation]
    D --> E[Quality Assessment]

Quality Framework

1. Documentation Quality

See quality_metrics for detailed criteria.

# @quality_framework
quality_metrics = {
    "completeness": {
        "required_sections": 0.95,  # 95% coverage
        "optional_sections": 0.80,  # 80% coverage
        "link_coverage": 0.90      # 90% link coverage
    },
    "consistency": {
        "style_compliance": 0.95,  # 95% style compliance
        "link_validity": 1.0,      # 100% valid links
        "metadata_validity": 1.0   # 100% valid metadata
    }
}

2. Validation Rules

See validation_framework for implementation.

# @validation_rules
validation_rules = {
    "structural": {
        "required_links": ["concept", "implementation"],
        "optional_links": ["example", "reference"]
    },
    "semantic": {
        "relationship_types": ["implements", "relates", "extends"],
        "metadata_fields": ["title", "type", "status"]
    }
}

Best Practices

1. Documentation Development

2. Knowledge Management

3. Quality Assurance

Related Documentation

References