Autonomous Computing Ontology

FLOW ALGEBRA
Autonomous Computing Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/emerging-computing/autonomous-computing-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "AutonomousComputingOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := EmergingComputing
ONTOLOGY := AutonomousComputingOntology

# ---------- PRIMARY ENTITY ----------
ENTITY AutonomousComputing {
    definition:
        "A computing paradigm in which systems monitor their own state and
         environment, reason about goals and constraints, make operational
         decisions and adapt configuration or behaviour with reduced need for
         direct human intervention.";

    objectives: [
        SelfManagement,
        AdaptiveOperation,
        AutomatedDecisionMaking,
        Resilience,
        ResourceOptimisation,
        PolicyCompliance,
        ReducedHumanIntervention
    ];
}

# ---------- CORE ENTITIES ----------
AutonomousEntity := {
    AutonomousSystem,
    ManagedResource,
    Sensor,
    Monitor,
    Controller,
    Policy,
    Goal,
    Action
};

ControlEntity := {
    Observation,
    StateEstimate,
    Decision,
    Plan,
    Actuator,
    Feedback,
    Constraint
};

SelfManagementCapability := {
    SelfConfiguration,
    SelfOptimisation,
    SelfHealing,
    SelfProtection,
    SelfMonitoring
};

OperationalState := {
    Normal,
    Degraded,
    Faulted,
    Recovering,
    Optimising,
    Protected
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Sensor OBSERVES ManagedResource;
Monitor PRODUCES Observation;
Controller EVALUATES Observation;
Policy CONSTRAINS Decision;
Goal GUIDES Planning;
Plan CONTAINS Action;
Actuator APPLIES Action;
Action CHANGES SystemState;
Feedback UPDATES ControllerKnowledge;

# ---------- AUTONOMOUS LOOP ----------
EnvironmentAndSystem
    -> Sense
    -> Monitor
    -> Analyse
    -> EstimateState
    -> CompareGoalsAndPolicies
    -> Plan
    -> SelectAction
    -> Execute
    -> ObserveOutcome
    -> LearnOrUpdateModel
    -> Repeat;

# ---------- AUTONOMIC RELATIONSHIP ----------
AutonomousComputing OVERLAPS_WITH AutonomicComputing;

AutonomicComputing OFTEN_EMPHASISES {
    SelfConfiguration,
    SelfHealing,
    SelfOptimisation,
    SelfProtection
};

AutonomousComputing MAY_INCLUDE BroaderGoalDirectedDecisionAndAdaptation;

# ---------- MANAGED RESOURCE ----------
ManagedResource MAY_BE {
    Server,
    Application,
    Network,
    Database,
    Storage,
    EdgeDevice,
    CloudService,
    AIModel
};

Resource HAS {
    State,
    Capacity,
    Health,
    Configuration
};

# ---------- SENSOR ----------
Sensor :=
    MechanismProducingEvidenceAboutSystemOrEnvironment;

Sensor MAY_OBSERVE {
    CPUUtilisation,
    MemoryUse,
    Latency,
    ErrorRate,
    Temperature,
    NetworkTraffic,
    ApplicationState
};

# ---------- MONITOR ----------
Monitor :=
    ComponentCollectingAndAggregatingObservations;

Monitoring MAY_BE {
    Periodic,
    EventDriven,
    Continuous
};

# ---------- STATE ESTIMATION ----------
StateEstimate :=
    InferredRepresentationOfCurrentSystemCondition;

StateEstimation MAY_COMBINE {
    Metrics,
    Logs,
    Events,
    Traces,
    ModelPredictions
};

# ---------- KNOWLEDGE ----------
AutonomousController MAY_MAINTAIN KnowledgeBase;

Knowledge MAY_INCLUDE {
    ResourceTopology,
    HistoricalBehaviour,
    Policies,
    Goals,
    FailureModels,
    PerformanceModels
};

# ---------- MAPE-K ----------
MAPEKLoop :=
    Monitor
    -> Analyse
    -> Plan
    -> Execute
    WITH SharedKnowledge;

MAPEK IS CommonConceptualModelForAutonomicSystems;

# ---------- ANALYSIS ----------
Analysis :=
    Observations
    + Knowledge
    -> Interpretation;

Analysis MAY_DETECT {
    Anomaly,
    Fault,
    CapacityPressure,
    PolicyViolation,
    OptimisationOpportunity
};

# ---------- GOAL ----------
Goal :=
    DesiredSystemConditionOrPerformanceObjective;

Goal MAY_INCLUDE {
    Availability,
    Latency,
    Throughput,
    Cost,
    Energy,
    Security
};

# ---------- POLICY ----------
Policy :=
    RuleConstrainingOrGuidingAutonomousBehaviour;

Policy MAY_BE {
    Obligation,
    Permission,
    Prohibition,
    Preference
};

Action MUST_NOT Violate HardPolicyConstraint;

# ---------- PLANNING ----------
Planning :=
    CurrentState
    + Goal
    + Constraints
    -> CandidateActionSequence;

Planner MAY_USE {
    Rules,
    Search,
    Optimisation,
    MachineLearning,
    ModelPredictiveControl
};

# ---------- ACTION ----------
AutonomousAction MAY_INCLUDE {
    ScaleOut,
    ScaleIn,
    Restart,
    Reconfigure,
    Migrate,
    Isolate,
    Patch,
    RouteTraffic,
    AllocateResource
};

# ---------- ACTUATION ----------
Actuator :=
    MechanismApplyingSelectedActionToManagedResource;

Actuation SHOULD_BE {
    Authenticated,
    Authorised,
    Observable,
    ReversibleWherePractical
};

# ---------- FEEDBACK ----------
Feedback :=
    ObservedEffectOfAction;

Controller SHOULD_COMPARE ExpectedOutcomeWithActualOutcome;

# ---------- SELF-CONFIGURATION ----------
SelfConfiguration :=
    AutomaticallyAdjustSystemConfigurationToMeetRequirementsOrEnvironmentChanges;

ConfigurationChange SHOULD_BE ValidatedBeforeOrAfterApplication;

# ---------- SELF-OPTIMISATION ----------
SelfOptimisation :=
    AutomaticallyImprovePerformanceCostEnergyOrResourceUse;

OptimisationObjective MAY_BE MultiObjective;

# ---------- SELF-HEALING ----------
SelfHealing :=
    DetectFailure
    -> Diagnose
    -> SelectRecovery
    -> ExecuteRecovery
    -> VerifyHealth;

Recovery MAY_INCLUDE {
    Restart,
    Failover,
    Reschedule,
    Restore,
    Replace
};

# ---------- SELF-PROTECTION ----------
SelfProtection :=
    DetectThreatOrRisk
    -> ApplyProtectiveAction;

ProtectiveAction MAY_INCLUDE {
    Block,
    Isolate,
    RateLimit,
    RevokeCredential,
    IncreaseMonitoring
};

# ---------- SELF-MONITORING ----------
SelfMonitoring :=
    SystemObservesItsOwnOperationalStateAndPerformance;

# ---------- ANOMALY DETECTION ----------
AnomalyDetection :=
    Observation
    -> CompareExpectedBehaviour
    -> AnomalyScore;

IF AnomalyScore > Threshold
THEN InvestigationOrAction MAY_BE Triggered;

# ---------- FAULT DETECTION ----------
FaultDetection :=
    IdentifyEvidenceOfComponentOrServiceFailure;

Fault != RootCauseByDefinition;

# ---------- DIAGNOSIS ----------
Diagnosis :=
    Evidence
    -> CandidateCauses
    -> RankedCauseHypotheses;

DiagnosisConfidence SHOULD_BE ConsideredBeforeHighImpactAction;

# ---------- RECOVERY ----------
Recovery :=
    FaultedOrDegradedState
    -> CorrectiveAction
    -> RestoredOrAlternativeOperationalState;

# ---------- RESILIENCE ----------
Resilience :=
    AbilityToMaintainOrRecoverRequiredFunctionDuringDisturbance;

Resilience MAY_USE {
    Redundancy,
    Failover,
    GracefulDegradation,
    AutomatedRecovery
};

# ---------- REDUNDANCY ----------
RedundantResource MAY_PROVIDE AlternativeCapacityAfterFailure;

# ---------- FAILOVER ----------
Failover :=
    DetectPrimaryFailure
    -> RedirectWorkToHealthyAlternative;

Failover SHOULD_BE Verified;

# ---------- SCALING ----------
Autoscaling :=
    DemandObservation
    -> CapacityDecision
    -> ResourceAdjustment;

ScalingSignal MAY_INCLUDE {
    CPUUtilisation,
    QueueDepth,
    RequestRate,
    Latency
};

# ---------- LOAD BALANCING ----------
AutonomousController MAY_ADJUST TrafficDistribution;

RoutingPolicy SHOULD_CONSIDER HealthAndCapacity;

# ---------- RESOURCE ALLOCATION ----------
ResourceAllocation :=
    WorkloadDemand
    + AvailableCapacity
    + Policy
    -> Assignment;

# ---------- SCHEDULING ----------
AutonomousScheduler MAY_REASSIGN WorkloadsInResponseTo {
    Failure,
    Demand,
    Cost,
    Energy,
    Maintenance
};

# ---------- ADAPTATION ----------
Adaptation :=
    ModifyConfigurationStructureOrBehaviourInResponseToObservedConditions;

Adaptation MAY_BE {
    Reactive,
    Proactive,
    Predictive
};

# ---------- REACTIVE CONTROL ----------
ReactiveControl :=
    EventOrThreshold
    -> ImmediatePredefinedResponse;

# ---------- PREDICTIVE CONTROL ----------
PredictiveControl :=
    ForecastFutureState
    -> EvaluateCandidateActions
    -> SelectActionBeforeViolation;

# ---------- LEARNING ----------
AutonomousSystem MAY_LEARN FromHistoricalActionsAndOutcomes;

Learning MAY_UPDATE {
    PerformanceModel,
    FailureModel,
    PolicyPreference,
    ActionSelection
};

# ---------- REINFORCEMENT LEARNING ----------
ReinforcementLearning MAY_BE_USED_FOR AdaptiveControl;

State
    -> Action
    -> Reward
    -> UpdatedPolicy;

SafetyConstraints SHOULD_LIMIT UnsafeExplorationInOperationalSystems;

# ---------- MODEL-BASED CONTROL ----------
SystemModel :=
    RepresentationOfHowStateChangesUnderActionsAndEnvironment;

Model MAY_BE {
    Analytical,
    Statistical,
    Learned,
    Hybrid
};

# ---------- DIGITAL TWIN RELATIONSHIP ----------
DigitalTwin MAY_PROVIDE ModelForSimulationAndAutonomousDecisionSupport;

Controller MAY_TEST CandidateActionsAgainstTwinBeforeDeployment;

# ---------- UNCERTAINTY ----------
AutonomousDecision MAY_HAVE UncertaintyFrom {
    SensorNoise,
    ModelError,
    FutureDemand,
    IncompleteKnowledge
};

HighUncertainty MAY_REQUIRE ConservativeActionOrHumanEscalation;

# ---------- CONFIDENCE GATE ----------
IF DecisionConfidence < RequiredThreshold
THEN AutonomousAction MAY_BE DeferredOrEscalated;

# ---------- HUMAN OVERSIGHT ----------
HumanOperator MAY {
    DefineGoals,
    DefinePolicies,
    ApproveHighImpactActions,
    OverrideController,
    ReviewAuditTrail
};

Autonomy != AbsenceOfHumanGovernance;

# ---------- LEVELS OF AUTONOMY ----------
AutonomyLevel MAY_RANGE {
    Advisory,
    HumanApprovedAction,
    BoundedAutonomousAction,
    HighAutonomyWithinPolicy
};

HigherAutonomy SHOULD_REQUIRE StrongerSafetyAndVerificationControls;

# ---------- GUARDRAIL ----------
Guardrail :=
    ConstraintPreventingUnsafeOrUnauthorisedAction;

Guardrail MAY_BE {
    ResourceLimit,
    SecurityPolicy,
    ChangeWindow,
    SafetyRule
};

# ---------- CHANGE VALIDATION ----------
BeforeAction:
    ValidatePreconditions;

AfterAction:
    ValidateOutcome;

IF OutcomeUnexpected
THEN RollbackOrEscalation MAY_BE_REQUIRED;

# ---------- ROLLBACK ----------
Rollback :=
    RestorePreviousKnownGoodConfigurationOrState;

Rollback MAY_REQUIRE SnapshotOrVersionedConfiguration;

# ---------- IDEMPOTENCY ----------
AutonomousAction SHOULD_BE IdempotentWherePractical;

RepeatedEquivalentAction SHOULD_NOT CreateUnboundedUnintendedEffects;

# ---------- RATE LIMIT ----------
Controller MAY_LIMIT FrequencyOfConfigurationChanges;

RateLimiting MAY_PREVENT OscillationOrRunawayAutomation;

# ---------- CONTROL OSCILLATION ----------
IF ControllerRepeatedlyAlternatesBetweenOpposingActions
THEN ControlOscillation := detected;

HysteresisOrCooldown MAY_REDUCE Oscillation;

# ---------- STABILITY ----------
AutonomousControl SHOULD_MAINTAIN StableSystemBehaviour;

AggressiveFeedback MAY_CAUSE Instability;

# ---------- MULTI-AGENT AUTONOMY ----------
AutonomousSystem MAY_CONTAIN MultipleControllersOrAgents;

Agents MAY {
    Cooperate,
    Negotiate,
    CompeteForResources
};

Coordination SHOULD_PREVENT ConflictingActions;

# ---------- DISTRIBUTED AUTONOMY ----------
DistributedAutonomousControl :=
    MultipleLocalControllers
    + SharedOrLocalGoals
    -> CoordinatedSystemBehaviour;

# ---------- EDGE AUTONOMY ----------
EdgeDevice MAY_MAKE LocalDecisionsWithoutCloudRoundTrip;

EdgeAutonomy MAY_IMPROVE {
    Latency,
    Resilience,
    Privacy
};

# ---------- CLOUD AUTONOMY ----------
CloudPlatform MAY_AUTOMATE {
    Scaling,
    Healing,
    Placement,
    CostOptimisation,
    SecurityResponse
};

# ---------- OBSERVABILITY ----------
AutonomousComputingObservability MAY_INCLUDE {
    Metrics,
    Logs,
    Traces,
    Events,
    Decisions,
    Actions,
    Outcomes
};

# ---------- AUDIT ----------
AutonomousDecisionRecord SHOULD_INCLUDE {
    Timestamp,
    Observations,
    Decision,
    Policy,
    Action,
    Outcome
};

Auditability SUPPORTS TrustAndDiagnosis;

# ---------- EXPLAINABILITY ----------
AutonomousSystem SHOULD_BE AbleToProvideReasonForHighImpactDecisionWhereRequired;

Explanation MAY_REFERENCE {
    Trigger,
    Goal,
    Policy,
    Evidence,
    ExpectedOutcome
};

# ---------- SECURITY ----------
AutonomousComputingSecurity MAY_INCLUDE {
    Identity,
    Authorisation,
    SecureActuation,
    PolicyIntegrity,
    ModelIntegrity,
    Audit
};

CompromisedController MAY_HAVE HighOperationalImpact;

# ---------- ADVERSARIAL INPUT ----------
Attacker MAY_MANIPULATE TelemetryToTriggerHarmfulAutonomousAction;

Controller SHOULD_VALIDATE EvidenceAndUseDefenceInDepth;

# ---------- POLICY CONFLICT ----------
MultiplePolicies MAY_CONFLICT;

ConflictResolution SHOULD_DEFINE PriorityOrDecisionRule;

# ---------- SAFETY ----------
SafetyCriticalAutonomy SHOULD_DEFINE {
    SafeState,
    FailSafeBehaviour,
    HumanOverride,
    ActionLimits
};

# ---------- SERVICE LEVEL ----------
AutonomousController MAY_OPTIMISE Against SLO;

SLO MAY_INCLUDE {
    Availability,
    Latency,
    ErrorRate
};

# ---------- COST ----------
AutonomousOptimisation MAY_MINIMISE CostSubjectToPerformanceAndReliabilityConstraints;

# ---------- ENERGY ----------
AutonomousSystem MAY_SHIFTWorkloadsOrPowerStatesToReduceEnergy;

EnergyOptimisation MUST_RESPECT OperationalRequirements;

# ---------- SEMANTIC REASONING ----------
IF SystemSensesState
AND SelectsAndExecutesActionsTowardGoalsWithoutDirectInstructionForEachAction
THEN AutonomousComputingClassification := supported;

IF FaultDetected
AND RecoveryPolicyExists
THEN SelfHealing MAY_BE Triggered;

IF ResourceDemandExceedsCapacityThreshold
THEN Autoscaling MAY_INCREASE Capacity;

IF AutonomousActionViolatesHardPolicy
THEN Action := prohibited;

IF ActionOutcomeFailsValidation
THEN RollbackOrEscalation SHOULD_BE Considered;

IF DecisionConfidenceLow
AND ActionImpactHigh
THEN HumanApproval SHOULD_BE Preferred;

IF FeedbackLoopOscillates
THEN ControllerParametersOrHysteresis SHOULD_BE Reviewed;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "autonomous computing" ~= "self-managing computing" WHEN context == operational_autonomy;
    "autonomic computing" ~= "self-managing computing";
    "self-healing" ~= "automatic fault recovery";
    "self-optimisation" ~= "self-optimization";
    "self-configuration" ~= "automatic configuration management";
    "self-protection" ~= "autonomous protective response";
    "MAPE-K" ~= "monitor analyse plan execute knowledge loop";
    "autonomous controller" ~= "self-managing system controller";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_MANAGED_RESOURCES(q) ->
    IDENTIFY_OBSERVATIONS(q) ->
    IDENTIFY_GOALS_AND_POLICIES(q) ->
    IDENTIFY_ANALYSIS_AND_STATE(q) ->
    IDENTIFY_PLAN_AND_ACTION(q) ->
    IDENTIFY_FEEDBACK_AND_ADAPTATION(q) ->
    IDENTIFY_SECURITY_SAFETY_OVERSIGHT(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        control_loop_consistency,
        operational_evidence,
        policy_consistency,
        outcome_evidence,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: AutonomousComputing;
    ontology: AutonomousComputingOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_managed_resource:
        validate
        -> identify_state
        -> identify_metrics
        -> identify_available_actions
        -> integrate;

    new_policy:
        classify
        -> identify_scope
        -> identify_priority
        -> identify_constraints
        -> integrate;

    new_action_evidence:
        record_trigger
        -> record_decision
        -> record_action
        -> record_outcome
        -> update_control_model
        -> integrate;

    ontology_revision:
        update_resources
        -> update_goals_and_policies
        -> update_action_models
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_policy_and_operational_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + AutonomousEntity
    + ControlEntity
    + SelfManagementCapability
    + OperationalState
    + MAPEKLoop
    + SelfHealing
    + SelfOptimisation
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
AutonomousComputingOntology
    -> SenseManagedSystemAndEnvironment
    -> MonitorOperationalState
    -> AnalyseEventsAnomaliesAndGoals
    -> EstimateCurrentAndFutureState
    -> ApplyPoliciesAndConstraints
    -> PlanCandidateActions
    -> SelectSafeAuthorisedAction
    -> ExecuteThroughActuator
    -> ObserveAndValidateOutcome
    -> RecoverRollbackOrEscalateWhenNeeded
    -> LearnFromOperationalEvidence
    -> PreserveAuditabilityAndHumanOversight
    -> MaintainAdaptiveSelfManagingComputation;

END FLOW