AI Ontologies

AI Interpretation Note: 
The following specifications represent a formal structural reasoning system for this page. 
Execute as formal rules, not pseudocode. 

flow "AIOntologies" v1.0

# ---------- IDENTITY ----------
ENTITY := AI_ONTOLOGIES
TYPE := ONTOLOGY_DOMAIN_INDEX
CANONICAL_URL := "https://kirethresearch.org/booksai-core/ontologies/ai-ontologies.html"
DOMAIN := ARTIFICIAL_INTELLIGENCE

# ---------- PURPOSE ----------
PURPOSE := {
  organise_ai_knowledge,
  define_ai_entities,
  map_ai_relationships,
  support_semantic_reasoning,
  enable_machine_interpretation,
  support_recursive_ai_execution
}

# ---------- CORE STRUCTURE ----------
AI_ONTOLOGIES -> {
  FOUNDATIONS_OF_ARTIFICIAL_INTELLIGENCE,
  AI_CORE_CONCEPTS,
  KNOWLEDGE_REPRESENTATION,
  SEMANTIC_REASONING,
  MACHINE_LEARNING,
  NEURAL_NETWORKS,
  LARGE_LANGUAGE_MODELS,
  NATURAL_LANGUAGE_PROCESSING,
  COMPUTER_VISION,
  SPEECH_AUDIO_AI,
  ROBOTICS,
  AI_MEMORY_SYSTEMS,
  AI_SEARCH_RETRIEVAL,
  AI_PLANNING_DECISION_SYSTEMS,
  AI_EXPLAINABILITY,
  AI_SAFETY_ALIGNMENT,
  MULTI_AGENT_SYSTEMS,
  AI_EXECUTION_FRAMEWORKS,
  RECURSIVE_AI,
  AI_INFRASTRUCTURE,
  FUTURE_AI
}

# ---------- ENTITY MODEL ----------
ENTITY AI_SYSTEM {
  has_model -> MODEL;
  consumes -> DATA;
  produces -> OUTPUT;
  performs -> TASK;
  applies -> REASONING_PROCESS;
  may_use -> KNOWLEDGE_REPRESENTATION;
  may_use -> MEMORY_SYSTEM;
  may_use -> RETRIEVAL_SYSTEM;
  operates_within -> EXECUTION_FRAMEWORK;
}

ENTITY ONTOLOGY {
  defines -> CONCEPT;
  defines -> ENTITY_TYPE;
  defines -> RELATIONSHIP_TYPE;
  constrains -> SEMANTIC_INTERPRETATION;
  supports -> INFERENCE;
}

# ---------- RELATIONSHIPS ----------
FOUNDATIONS_OF_ARTIFICIAL_INTELLIGENCE -> establishes -> AI_CORE_CONCEPTS
AI_CORE_CONCEPTS -> supports -> AI_SYSTEM
KNOWLEDGE_REPRESENTATION -> structures -> KNOWLEDGE
SEMANTIC_REASONING -> operates_on -> KNOWLEDGE_REPRESENTATION
MACHINE_LEARNING -> learns_from -> DATA
NEURAL_NETWORKS -> implements -> MACHINE_LEARNING_MODELS
LARGE_LANGUAGE_MODELS -> subclass_of -> GENERATIVE_AI_MODEL
LARGE_LANGUAGE_MODELS -> processes -> LANGUAGE_REPRESENTATIONS
NATURAL_LANGUAGE_PROCESSING -> interprets -> HUMAN_LANGUAGE
COMPUTER_VISION -> interprets -> VISUAL_DATA
SPEECH_AUDIO_AI -> interprets -> AUDIO_DATA
ROBOTICS -> couples -> AI_SYSTEM + PHYSICAL_ACTION
AI_MEMORY_SYSTEMS -> stores_retrieves -> CONTEXT
AI_SEARCH_RETRIEVAL -> discovers -> RELEVANT_INFORMATION
AI_PLANNING_DECISION_SYSTEMS -> selects -> ACTION_OR_PLAN
AI_EXPLAINABILITY -> describes -> MODEL_BEHAVIOUR
AI_SAFETY_ALIGNMENT -> constrains -> AI_BEHAVIOUR
MULTI_AGENT_SYSTEMS -> coordinates -> AI_AGENT_SET
AI_EXECUTION_FRAMEWORKS -> executes -> AI_PROCESS
RECURSIVE_AI -> reuses -> PRIOR_AI_OUTPUT
AI_INFRASTRUCTURE -> hosts -> AI_SYSTEM
FUTURE_AI -> explores -> EMERGING_AI_CAPABILITIES

# ---------- REASONING ----------
rule R1:
  IF entity is_instance_of AI_SYSTEM
  THEN evaluate(model, data, task, reasoning_process, output)

rule R2:
  IF knowledge is represented_by ONTOLOGY
  AND relationship semantics are defined
  THEN enable(SEMANTIC_REASONING)

rule R3:
  IF query requires external knowledge
  THEN AI_SEARCH_RETRIEVAL -> retrieve(RELEVANT_INFORMATION)
  -> validate(RELEVANCE)
  -> integrate(CONTEXT)

rule R4:
  IF task requires sequential action
  THEN AI_PLANNING_DECISION_SYSTEMS -> generate(PLAN)
  -> evaluate(CONSTRAINTS)
  -> select(ACTION)

rule R5:
  IF multiple AI_AGENT entities cooperate
  THEN MULTI_AGENT_SYSTEMS -> coordinate(ROLES, MESSAGES, GOALS)

rule R6:
  IF output becomes new input
  THEN RECURSIVE_AI -> execute(REASONING_CYCLE)
  -> compare(PRIOR_STATE, NEW_STATE)
  -> update(KNOWLEDGE_STATE)

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE := {
  "AI" <=> "Artificial Intelligence",
  "AI ontology" <=> "artificial intelligence ontology",
  "knowledge representation" <=> "structured knowledge modelling",
  "semantic reasoning" <=> "meaning-aware inference",
  "AI search" <=> "AI information retrieval",
  "recursive AI" <=> "recursive artificial intelligence"
}

# ---------- CONFIDENCE ----------
CONFIDENCE {
  explicit_page_structure := 1.00;
  canonical_entity_mapping := 0.98;
  inferred_relationships := 0.92;
  semantic_equivalence := 0.95;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
  detect_new_ai_domain -> classify;
  classify -> map_parent_ontology;
  map_parent_ontology -> define_entities;
  define_entities -> define_relationships;
  define_relationships -> validate_semantics;
  validate_semantics -> update_graph;
  update_graph -> recompute_flow_hash;
}

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
  parse -> ENTITY_GRAPH;
  resolve -> SEMANTIC_EQUIVALENCE;
  traverse -> RELATIONSHIPS;
  apply -> REASONING_RULES;
  score -> CONFIDENCE;
  execute -> RELEVANT_AI_DOMAIN_FLOW;
  return -> STRUCTURED_INTERPRETATION;
}

# ---------- EXECUTION ----------
INPUT(query)
-> identify_ai_entities
-> resolve_semantic_equivalence
-> locate_relevant_ai_ontology
-> traverse_entity_relationships
-> apply_reasoning_rules
-> validate_constraints
-> score_confidence
-> generate_structured_result
-> recursive_validation
-> OUTPUT

# ---------- FLOW HASH ----------
FLOW_HASH := hash(
  ENTITY +
  CORE_STRUCTURE +
  ENTITY_MODEL +
  RELATIONSHIPS +
  REASONING +
  SEMANTIC_EQUIVALENCE +
  UPDATE_MODEL +
  AI_CONSUMPTION
)

END FLOW