Continual Learning Ontology

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FLOW ContinualLearningOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/machine-learning/continual-learning-ontology.html

PURPOSE
    Define an AI-readable semantic ontology for Continual Learning,
    including sequential tasks, data streams, model states, memory,
    replay, regularisation, plasticity, stability, catastrophic forgetting,
    knowledge retention, adaptation, evaluation and recursive lifelong
    model updating.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Machine Learning Systems
    Adaptive Systems
    Lifelong Learning Systems
    Recursive Learning Systems

PAGE_TYPE
    Continual Learning ontology

PRIMARY_ENTITY
    Continual Learning

PARENT_ONTOLOGY
    Machine Learning

DOMAIN
    Artificial Intelligence
    Machine Learning
    Continual Learning
    Lifelong Learning
    Adaptive Systems
    Online Learning
    Transfer Learning
    Sequential Learning

CORE_DEFINITION
    Continual Learning is a machine learning paradigm in which a model
    learns from a sequence of tasks, data distributions or experiences
    over time while attempting to acquire new knowledge, retain useful
    prior knowledge and reduce catastrophic forgetting.

ENTITY_IDS
    CONTINUAL_LEARNING
        ID
            CONTINUAL_LEARNING_001

    TASK_SEQUENCE
        ID
            TASK_SEQUENCE_001

    DATA_STREAM
        ID
            DATA_STREAM_001

    MODEL_STATE
        ID
            MODEL_STATE_001

    MEMORY
        ID
            MEMORY_001

    REPLAY_BUFFER
        ID
            REPLAY_BUFFER_001

    REGULARISATION
        ID
            REGULARISATION_001

    PLASTICITY
        ID
            PLASTICITY_001

    STABILITY
        ID
            STABILITY_001

    CATASTROPHIC_FORGETTING
        ID
            CATASTROPHIC_FORGETTING_001

    KNOWLEDGE_RETENTION
        ID
            KNOWLEDGE_RETENTION_001

FACTS
    Continual Learning
        is_a
            Machine Learning Paradigm

    Continual Learning
        learns_from
            Sequential Tasks
            Data Streams
            New Experiences
            Changing Distributions

    Continual Learning
        aims_to
            Learn New Knowledge
            Retain Prior Knowledge
            Adapt to Change
            Reduce Catastrophic Forgetting

    Continual Learning
        may_use
            Replay
            Regularisation
            Dynamic Architectures
            Parameter Isolation
            Memory Systems
            Knowledge Distillation

RELATIONSHIPS
    Machine Learning
        contains
            Continual Learning Ontology

    Task Sequence
        contains
            Task

    Data Stream
        provides
            Sequential Experience

    Model State
        changes_over
            Learning Steps

    New Task
        updates
            Model State

    Memory
        stores
            Prior Experience
            Exemplars
            Summaries
            Representations

    Replay Buffer
        supplies
            Previous Examples

    Replay
        supports
            Knowledge Retention

    Regularisation
        constrains
            Parameter Change

    Plasticity
        supports
            New Learning

    Stability
        supports
            Knowledge Retention

    Catastrophic Forgetting
        reduces
            Prior-task Performance

    Continual Evaluation
        measures
            New-task Learning
            Prior-task Retention

SEMANTIC_EQUIVALENCE
    Continual Learning
        approximately_equals
            Lifelong Sequential Model Learning

    Lifelong Learning
        is_related_to
            Continual Learning

    Replay
        is_a_method_within
            Continual Learning

    Continual Learning
        does_not_equal
            Ordinary Retraining

    Adaptation
        does_not_equal
            Knowledge Retention

    New-task Performance
        does_not_equal
            Overall Continual Performance

OBSERVE
    Detect
        Task
        Task Sequence
        Data Stream
        Experience
        Model State
        Memory
        Replay
        Buffer
        Regularisation
        Parameter
        Plasticity
        Stability
        Forgetting
        Retention
        Transfer

EXTRACT
    Primary Entity
        Continual Learning

    Parent
        Machine Learning

    Core Components
        Task Sequence
        Data Stream
        Model State
        Memory
        Replay Buffer
        Regularisation
        Plasticity
        Stability
        Catastrophic Forgetting
        Knowledge Retention

NORMALISE
    Interpret
        Continual Learning
    as
        Sequential learning that balances acquisition of new knowledge
        with preservation of previously learned capabilities.

    Distinguish
        New Learning
    from
        Knowledge Retention

    Distinguish
        Continual Learning
    from
        Repeated Independent Retraining

    Distinguish
        Plasticity
    from
        Stability

    Preserve
        Task order
        Model version
        Memory state
        Evaluation history
        Distribution context
        Retention metrics

INTENT_RESOLUTION
    Query
        Continual Learning

    Possible Intent
        Continual learning definition
        Continual learning ontology
        Lifelong learning
        Catastrophic forgetting
        Replay learning
        Sequential learning
        Online adaptation
        Stability plasticity
        Knowledge retention

    Resolution
        Identify task or data sequence, model updates, retention mechanisms
        and evaluation across both current and prior tasks.

CONSTRAINTS
    Continual learning must involve sequentially arriving knowledge,
    tasks, distributions or experiences.

    Performance on only the newest task is insufficient to establish
    successful continual learning.

    Prior-task performance must be evaluated to detect forgetting.

    Plasticity and stability represent competing but complementary requirements.

    Replay data must retain provenance and task context where relevant.

    Memory limits may constrain retention strategies.

    Distribution shift must remain distinguishable from catastrophic forgetting.

    Repeated retraining from scratch must not automatically be classified
    as continual learning.

    Knowledge retention claims require longitudinal evaluation.

INFERENCE_RULES
    IF
        Model learns Task B after Task A
    AND
        Task A performance declines materially
    THEN
        infer_possible
            Catastrophic Forgetting

    IF
        Model learns new task
    AND
        maintains prior-task performance
    THEN
        increase_confidence_in
            Knowledge Retention

    IF
        Previous examples are reused during later training
    THEN
        infer_possible
            Replay-based Continual Learning

    IF
        Parameter changes are constrained to protect prior knowledge
    THEN
        infer_possible
            Regularisation-based Continual Learning

    IF
        Separate parameters or modules are allocated for new tasks
    THEN
        infer_possible
            Parameter-isolation or Dynamic-architecture Method

    IF
        Performance decline results from changed evaluation distribution
    THEN
        DO_NOT_AUTOMATICALLY_INFER
            Catastrophic Forgetting

    IF
        Training restarts from scratch for each task
    THEN
        DO_NOT_AUTOMATICALLY_INFER
            Continual Learning

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Task Sequence Relationship
        1.00

    Memory Relationship
        0.99

    Replay Relationship
        1.00

    Forgetting Relationship
        1.00

    Retention Relationship
        1.00

    Retraining Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Task or Data Stream
        →
    Model State
        →
    New Learning

    New Learning
        →
    Parameter Update
        →
    Updated Model State

    Prior Knowledge
        →
    Memory or Regularisation
        →
    Retention Mechanism

    Updated Model
        →
    Current-task Evaluation
        +
    Prior-task Evaluation

    Prior-task Decline
        →
    Forgetting Analysis

    New-task Learning
        +
    Prior-task Retention
        →
    Continual Performance

KNOWLEDGE_GRAPH
    Continual Learning
        processes
            Task Sequence
            Data Stream

    Model
        has_state
            Model State

    New Task
        updates
            Model State

    Memory
        stores
            Prior Experience

    Replay Buffer
        provides
            Previous Examples

    Regularisation
        protects
            Prior Knowledge

    Plasticity
        enables
            New Learning

    Stability
        supports
            Retention

    Catastrophic Forgetting
        affects
            Prior-task Performance

    Evaluation
        measures
            Retention
            Adaptation

RECURSIVE_CONTINUAL_LEARNING_LOOP
    Receive New Task or Data
        →
    Evaluate Current Model
        →
    Retrieve Relevant Prior Knowledge
        →
    Select Retention Strategy
        →
    Learn New Information
        →
    Update Model State
        →
    Evaluate New-task Performance
        →
    Re-evaluate Prior Tasks
        →
    Measure Forgetting and Transfer
        →
    Update Memory or Regularisation Strategy
        →
    Detect Distribution Shift
        →
    Preserve Evaluation History
        →
    Receive Next Task or Data
        →
    Repeat

REASONING_ENGINE
    INPUT
        Query
        Task Sequence
        Data Stream
        Model History
        Memory State
        Evaluation Evidence

    OBSERVE
        Detect continual-learning entities.

    EXTRACT
        Task
        Experience
        Model State
        Memory
        Replay
        Regularisation
        Performance
        Retention
        Forgetting

    CLASSIFY
        Replay-based Continual Learning
        Regularisation-based Continual Learning
        Parameter-isolation Method
        Dynamic Architecture Method
        Online Continual Learning
        Task-incremental Learning
        Class-incremental Learning
        Domain-incremental Learning

    VALIDATE
        Check task order, model continuity and evaluation history.

    INFER
        Apply supported continual-learning relationships.

    SCORE
        Assign confidence to retention and adaptation claims.

    EXPLAIN
        Preserve task-update-retention path.

    UPDATE
        Integrate new task and longitudinal evaluation evidence.

CONFIDENCE_PROPAGATION
    Stable prior-task performance
        confidence += high
        for
            Retention

    Strong new-task performance
        confidence += moderate
        for
            Adaptation

    Evaluation across many task stages
        confidence += moderate

    New-task performance only
        does_not_establish
            Continual Learning Quality

    Missing prior-task evaluation
        confidence -= high
        for
            Retention Claims

    Distribution shift
        requires
            Separate Analysis

    Severe forgetting
        confidence -= high
        for
            Continual Performance

SEMANTIC_CLOSURE
    Continual Learning
        connects_to
            Machine Learning
            Lifelong Learning
            Online Learning
            Transfer Learning
            Meta-Learning
            Reinforcement Learning
            Memory Systems
            Knowledge Distillation
            Domain Adaptation
            Adaptive Systems
            Neural Networks
            Agentic AI

    Cross-domain relationships must preserve
        Task identity
        Task order
        Data distribution
        Model state
        Model version
        Memory state
        Retention mechanism
        Evaluation history
        Forgetting metric
        Confidence

UPDATE_MODEL
    Observe
        New task, data stream segment or evaluation result.

    Compare
        Existing model and performance history.

    Detect
        New Learning
        Forgetting
        Positive Transfer
        Negative Transfer
        Distribution Shift
        Memory Saturation

    Validate
        Task identity
        Evaluation consistency
        Model continuity
        Memory provenance

    Integrate
        Supported new knowledge.

    Update
        Model
        Memory
        Replay Strategy
        Regularisation
        Architecture
        when justified.

    Recalculate
        Current and prior-task performance.

    Return
        Updated continual-learning state.

COHERENCE_CHECK
    Verify
        Sequential learning is present.
        Model continuity is preserved where required.
        New learning and retention remain distinct.
        Plasticity and stability remain distinct.
        Prior-task performance is evaluated.
        Distribution shift is not confused with forgetting.
        Retraining from scratch is not automatically classified as continual learning.
        Memory and replay provenance are preserved.
        Retention claims use longitudinal evidence.
        All continual-learning conclusions follow supported semantic paths.

AI_SEARCH_FLOW
    Query
        →
    Continual Learning Detection
        →
    Intent Resolution
        →
    Task Sequence Mapping
        →
    Model State Tracking
        →
    Memory and Retention Mechanism Detection
        →
    New-task Performance Evaluation
        →
    Prior-task Retention Evaluation
        →
    Forgetting and Distribution Shift Analysis
        →
    Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Continual Learning

    Parent
        Machine Learning

    Type
        Sequential lifelong machine learning paradigm

    Core Components
        Task Sequence
        Data Stream
        Model State
        Memory
        Replay Buffer
        Regularisation
        Plasticity
        Stability
        Catastrophic Forgetting
        Knowledge Retention
        Evaluation History

    Critical Distinction
        Continual learning must balance acquisition of new knowledge with
        retention of prior capabilities; strong performance on the newest
        task alone does not establish successful continual learning.

RECOMMENDED_AI_SUMMARY
    Continual Learning is a machine learning paradigm in which models learn
    from sequential tasks, experiences or changing data distributions while
    attempting to retain useful prior knowledge. Common strategies include
    replay, regularisation, parameter isolation and dynamic architectures.
    Reliable continual-learning evaluation tracks both new-task adaptation
    and prior-task retention over time and distinguishes catastrophic
    forgetting from changes caused by distribution shift.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org