Bio-Inspired Computing Ontology

FLOW ALGEBRA
Bio-Inspired Computing Ontology
Kireth Semantic Research Archive

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

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "BioInspiredComputingOntology" v4.0

DOMAIN := ComputingOntology
SUBDOMAIN := EmergingComputing
ONTOLOGY := BioInspiredComputingOntology

ENTITY BioInspiredComputing {
    definition:
        "A computing paradigm that derives computational models, algorithms,
         architectures or adaptive strategies from principles observed in
         biological systems, including evolution, neural processing,
         collective behaviour, immune response and self-organisation.";
    objectives: [
        AdaptiveComputation,
        EvolutionarySearch,
        CollectiveIntelligence,
        SelfOrganisation,
        RobustOptimisation,
        BrainInspiredProcessing,
        ResilientSystems
    ];
}

BioInspiredEntity := {
    BiologicalSystem,
    ComputationalModel,
    Population,
    Agent,
    Individual,
    Environment,
    FitnessFunction,
    Adaptation
};

ParadigmEntity := {
    EvolutionaryComputing,
    GeneticAlgorithm,
    SwarmIntelligence,
    ArtificialNeuralNetwork,
    NeuromorphicComputing,
    ArtificialImmuneSystem,
    CellularAutomata,
    DevelopmentalComputing
};

ProcessEntity := {
    Selection,
    Mutation,
    Recombination,
    Learning,
    Interaction,
    SelfOrganisation,
    Feedback,
    Adaptation
};

BiologicalPrinciple INSPIRES ComputationalModel;
ComputationalModel IMPLEMENTS AlgorithmOrArchitecture;
Population CONSISTS_OF IndividualsOrAgents;
Environment INFLUENCES AgentBehaviour;
FitnessFunction EVALUATES CandidateSolution;
Selection FAVOURS SelectedCandidates;
Mutation MODIFIES CandidateRepresentation;
Interaction MAY_CREATE EmergentBehaviour;

BioInspiredFlow :=
    ComputationalProblem
    -> IdentifyBiologicalPrinciple
    -> AbstractRelevantMechanism
    -> DefineComputationalRepresentation
    -> InitialisePopulationOrSystem
    -> ExecuteInteractionLearningOrEvolution
    -> EvaluatePerformance
    -> AdaptStateOrParameters
    -> RepeatUntilStoppingCondition
    -> SelectOrDecodeSolution
    -> CompareWithBaseline;

BiologicalInspiration MAY_COME_FROM {
    Evolution,
    NervousSystems,
    ImmuneSystems,
    SocialOrganisms,
    Development,
    Morphogenesis,
    Ecosystems,
    CellularProcesses
};

BioInspiredModel DOES_NOT_REQUIRE ExactBiologicalReplication;

BiologicalAbstraction :=
    ObservedBiologicalMechanism
    -> IdentifyFunctionalPrinciple
    -> RemoveUnnecessaryBiologicalDetail
    -> ConstructComputationalMechanism;

EvolutionaryComputing :=
    PopulationOfCandidateSolutions
    -> EvaluateFitness
    -> Select
    -> RecombineOrMutate
    -> CreateNewPopulation
    -> Repeat;

EvolutionaryAlgorithm MAY_INCLUDE {
    GeneticAlgorithm,
    EvolutionStrategy,
    GeneticProgramming,
    DifferentialEvolution
};

Individual :=
    CandidateSolutionWithinPopulation;

Individual HAS {
    GenotypeOrRepresentation,
    PhenotypeOrDecodedSolution,
    Fitness
};

Population :=
    SET_OF(CandidateSolutions);

PopulationDiversity MAY_SUPPORT Exploration;
LowDiversity MAY_INCREASE PrematureConvergenceRisk;

FitnessFunction :=
    Mapping(CandidateSolution -> PerformanceScoreOrVector);

Fitness SHOULD_REFLECT OptimisationObjectiveAndConstraints;

Selection :=
    ChooseIndividualsForReproductionOrSurvivalBasedOnDefinedPolicy;

HighSelectionPressure MAY_ACCELERATE Convergence
BUT MAY_REDUCE Diversity;

Mutation :=
    RandomOrStochasticModificationOfCandidateRepresentation;

Mutation MAY_INTRODUCE NewVariation;

Recombination :=
    CombineInformationFromMultipleParentCandidates
    -> Offspring;

GeneticAlgorithm :=
    InitialisePopulation
    -> EvaluateFitness
    -> SelectParents
    -> Crossover
    -> Mutate
    -> EvaluateOffspring
    -> SelectNextGeneration
    -> Repeat;

GeneticProgramming :=
    EvolutionarySearchOverProgramStructures;

EvolutionStrategy :=
    EvolutionaryOptimisationEmphasisingMutationAndStrategyParameters;

DifferentialEvolution :=
    PopulationBasedContinuousOptimisationUsingScaledDifferencesBetweenCandidateVectors;

MultiObjectiveEvolution :=
    OptimiseMultiplePotentiallyConflictingObjectives;

ParetoFront :=
    SetOfNonDominatedSolutions;

SwarmIntelligence :=
    DecentralisedCollectiveComputationInspiredBySocialOrganisms;

Swarm MAY_CONSIST_OF {
    Agents,
    LocalRules,
    Environment,
    Communication
};

GlobalBehaviour MAY_EMERGE_FROM LocalInteractions;

ParticleSwarmOptimisation :=
    PopulationOfParticles
    -> EvaluatePositions
    -> UpdateVelocityFromPersonalAndSocialInformation
    -> UpdatePosition
    -> Repeat;

Particle HAS {
    Position,
    Velocity,
    PersonalBest
};

AntColonyOptimisation :=
    AgentsConstructSolutions
    -> DepositVirtualPheromone
    -> EvaporatePheromone
    -> BiasFutureSearch
    -> Repeat;

Pheromone REPRESENTS DistributedSearchMemory;

FlockingModel MAY_USE {
    Separation,
    Alignment,
    Cohesion
};

ComplexCollectiveMotion MAY_EMERGE_FROM SimpleLocalRules;

ArtificialNeuralNetwork :=
    NetworkOfComputationalUnitsHistoricallyInspiredByNeuralProcessing;

ModernANN IS HighlyAbstractedFrom BiologicalNeuralSystems;

NeuromorphicComputing :=
    ArchitectureAndComputationInspiredByNeuralStructureAndDynamics;

NeuromorphicSystem MAY_USE {
    SpikingNeurons,
    Synapses,
    EventDrivenCommunication,
    LocalMemory
};

SpikingNeuralNetwork :=
    NetworkWhereInformationMayBeRepresentedByDiscreteSpikeEventsAndTiming;

HebbianPrinciple :=
    SynapticChangeRelatedToCorrelatedActivityOfConnectedUnits;

SpikeTimingDependentPlasticity :=
    SynapticUpdateDependingOnRelativeTimingOfPreAndPostsynapticSpikes;

ArtificialImmuneSystem :=
    ComputationalMethodsInspiredByImmuneRecognitionAdaptationAndMemory;

ArtificialImmuneSystem MAY_MODEL {
    Detection,
    ClonalSelection,
    NegativeSelection,
    ImmuneMemory,
    Diversity
};

ClonalSelectionAlgorithm :=
    DetectHighAffinityCandidates
    -> Clone
    -> Mutate
    -> SelectImprovedCandidates;

NegativeSelection :=
    GenerateDetectors
    -> RemoveDetectorsMatchingSelf
    -> UseRemainingDetectorsToIdentifyNonselfOrAnomalies;

CellularAutomaton :=
    GridOrGraphOfCells
    + LocalState
    + LocalUpdateRule
    -> GlobalDynamics;

Emergence :=
    SystemLevelBehaviourArisingFromInteractionsAmongComponentsWithoutDirectCentralSpecificationOfFullGlobalPattern;

SelfOrganisation :=
    FormationOfStructuredGlobalBehaviourThroughLocalInteractionsAndFeedback;

DevelopmentalComputing :=
    ComputationInspiredByBiologicalGrowthDifferentiationAndMorphogenesis;

Morphogenesis :=
    ProcessByWhichLocalRulesAndSignalsGenerateSpatialStructure;

EmbryogenicComputing :=
    ComputingApproachInspiredByDevelopmentGrowthRepairAndDifferentiation;

Coevolution :=
    EvolutionOfMultipleInteractingPopulationsWhoseFitnessDependsOnEachOther;

Adaptation :=
    ChangeInSystemStateParametersOrStructureInResponseToPerformanceOrEnvironment;

Plasticity :=
    AbilityOfSystemToModifyInternalOrganisationOrResponseBasedOnExperience;

Robustness :=
    AbilityToMaintainFunctionUnderNoisePerturbationOrComponentFailure;

Resilience :=
    AbilityToRecoverOrAdaptAfterDisturbance;

Homeostasis :=
    FeedbackProcessMaintainingSystemVariablesWithinFunctionalRange;

Feedback MAY_BE {
    PositiveFeedback,
    NegativeFeedback
};

DecentralisedSystem :=
    NoSingleControllerRequiredForAllDecisions;

LocalAgents MAY_USE LocalInformation;

PopulationDiversity :=
    VariationAmongCandidateSolutionsOrAgents;

Exploration :=
    SearchNewRegionsOfSolutionSpace;

Exploitation :=
    RefinePromisingKnownRegions;

BioInspiredOptimisation SHOULD_BALANCE ExplorationAndExploitation;

BioInspiredAlgorithm MAY_USE RandomnessFor {
    Mutation,
    Sampling,
    AgentMovement,
    Selection
};

RandomSeed SHOULD_BE RecordedForReproducibilityWhereRelevant;

FitnessLandscape :=
    MappingFromCandidateRepresentationToFitness;

FitnessLandscape MAY_HAVE {
    LocalOptima,
    Plateaus,
    Ridges,
    MultipleBasins
};

ConstraintHandling MAY_USE {
    Penalty,
    Repair,
    FeasibilityRules,
    SpecialRepresentation
};

StoppingCondition MAY_INCLUDE {
    MaximumIterations,
    TargetFitness,
    NoImprovement,
    ResourceBudget
};

Performance MAY_BE_MEASURED_BY {
    SolutionQuality,
    ConvergenceRate,
    Runtime,
    Robustness,
    Energy
};

BioInspiredAlgorithm SHOULD_BE ComparedAgainst CompetitiveBaselineAlgorithms;

StochasticAlgorithms SHOULD_BE EvaluatedAcrossMultipleIndependentRuns;

Report MAY_INCLUDE {
    Mean,
    Median,
    Variance,
    Best,
    Worst,
    ConfidenceInterval
};

SingleBestRun IS_INSUFFICIENT_FOR RobustPerformanceClaim;

FitnessEvaluationCost MAY_DOMINATE Runtime;

PopulationAndAgentEvaluations MAY_BE Parallelisable;

BioInspiredComputing MAY_RUN_ON {
    CPU,
    GPU,
    FPGA,
    NeuromorphicHardware,
    DistributedSystem
};

BioInspiredRobotics MAY_COMBINE {
    BodyMorphology,
    SensorimotorLoop,
    AdaptiveController,
    Environment
};

SwarmRobotics :=
    MultipleRobotsUsingLocalRulesAndInteractionsToAchieveCollectiveBehaviour;

BioInspiredComputing MAY_SUPPORT {
    Optimisation,
    MachineLearning,
    Robotics,
    Scheduling,
    Routing,
    AnomalyDetection,
    Control,
    PatternRecognition
};

BiologicalFidelity :=
    DegreeToWhichComputationalModelMatchesBiologicalMechanism;

HighBiologicalFidelity IS_NOT_REQUIRED_FOR ComputationalUsefulness;

BioInspiredAdaptiveSystems MAY_CREATE SecurityRisksThrough {
    UnpredictableAdaptation,
    AdversarialFitnessManipulation,
    EmergentBehaviour
};

# ---------- SEMANTIC REASONING ----------
IF ComputationalMethodAbstractsPrinciplesFromBiologicalSystems
THEN BioInspiredComputingClassification := supported;

IF PopulationDiversityCollapsesEarly
THEN PrematureConvergenceRisk MAY_INCREASE;

IF SelectionPressureVeryHigh
THEN Exploration MAY_DECREASE;

IF MutationRateTooLow
THEN NovelVariation MAY_DECREASE;

IF MutationRateTooHigh
THEN UsefulStructure MAY_BE Disrupted;

IF LocalAgentRulesProduceGlobalPattern
THEN EmergentBehaviour := supported;

IF StochasticAlgorithmEvaluatedOnSingleRun
THEN PerformanceConfidence := low;

IF ClaimedBioInspiredAdvantageLacksCompetitiveBaseline
THEN AdvantageConfidence := low;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "bio-inspired computing" ~= "biologically inspired computing";
    "nature-inspired computing" ~= "bio-inspired computing" WHEN biological_source_is_primary;
    "evolutionary computing" ~= "evolutionary computation";
    "genetic algorithm" ~= "GA";
    "particle swarm optimisation" ~= "PSO";
    "ant colony optimisation" ~= "ACO";
    "artificial immune system" ~= "AIS" WHEN context == computing;
    "swarm intelligence" ~= "collective bio-inspired intelligence";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_BIOLOGICAL_PRINCIPLE(q) ->
    IDENTIFY_COMPUTATIONAL_ABSTRACTION(q) ->
    IDENTIFY_POPULATION_OR_NETWORK(q) ->
    IDENTIFY_ADAPTATION_OR_LEARNING(q) ->
    IDENTIFY_FITNESS_OR_OBJECTIVE(q) ->
    IDENTIFY_EMERGENCE_ROBUSTNESS(q) ->
    IDENTIFY_PERFORMANCE_AND_BASELINE(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        computational_model_consistency,
        biological_inspiration_consistency,
        experimental_evidence,
        benchmark_quality,
        contextual_relevance
    );

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

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: BioInspiredComputing;
    ontology: BioInspiredComputingOntology;
    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_biological_principle:
        validate
        -> identify_source_system
        -> identify_functional_mechanism
        -> identify_computational_abstraction
        -> integrate;

    new_algorithm:
        classify_paradigm
        -> identify_representation
        -> identify_adaptation
        -> identify_objective
        -> identify_stopping_condition
        -> integrate;

    new_benchmark:
        record_problem
        -> record_baseline
        -> record_evaluation_budget
        -> record_multiple_runs
        -> record_statistics
        -> integrate;

    ontology_revision:
        update_paradigm_relationships
        -> update_performance_evidence
        -> update_biological_mapping
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + BioInspiredEntity
    + ParadigmEntity
    + ProcessEntity
    + EvolutionaryComputing
    + SwarmIntelligence
    + ArtificialImmuneSystem
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
BioInspiredComputingOntology
    -> IdentifyBiologicalPrinciple
    -> AbstractComputationalMechanism
    -> DefinePopulationNetworkOrAgents
    -> InitialiseCandidateStates
    -> ExecuteLearningEvolutionOrInteraction
    -> EvaluateFitnessOrObjective
    -> PreserveDiversityAndAdaptation
    -> DetectEmergentBehaviour
    -> SelectOrDecodeSolution
    -> MeasureRobustnessPerformanceAndCost
    -> CompareCompetitiveBaseline
    -> RecordStochasticEvidenceAndProvenance
    -> MaintainAdaptiveBioInspiredComputation;

END FLOW