Artificial Object Ontology

FLOW ALGEBRA
ARTIFICIAL OBJECT ONTOLOGY
Version: 1.1
Date: 2026-10-12

[01] ONTOLOGY IDENTITY
ontology_id: KRL:PhysicalEntities:ArtificialEntities:ArtificialObject
label: Artificial Object Ontology
parent: Artificial Entities
category: Physical Entity / Designed Artefact
version: 1.1
status: scientific synthesis; source page not independently verified

[02] SCOPE
An artificial object is a physical artefact intentionally made, assembled, substantially modified or configured through an agent-directed process. Its intended function is distinct from its actual capabilities and observed use.

[03] FORMAL UNIVERSE
E: set of physical entities
A subset E: artificial objects
G: agents
P: production processes
F: functions
M: materials
T: times
C: contexts

[04] CORE PREDICATES
ArtificialObject(x)
PhysicalEntity(x)
ProducedBy(x,p)
AgentDirected(p,g)
DesignedFor(x,f)
Performs(x,f,t,c)
MadeOf(x,m)
PartOf(x,y,t)
HasComponent(x,y,t)
ExistsAt(x,t)

[05] TYPED ENTITIES
Artefact : PhysicalEntity
Agent : Person | Organisation | AutomatedSystem
Design : InformationObject
Specification : InformationObject
Material : MaterialEntity
Component : PhysicalEntity
Assembly : ArtificialObject
ManufacturingProcess : Process
Function : DispositionOrPurpose
UseEvent : Event

[06] DEFINITION RULE
ArtificialObject(x) -> PhysicalEntity(x).
ProducedBy(x,p) AND AgentDirected(p,g) AND ResultOf(p,x) -> CandidateArtificialObject(x).
CandidateArtificialObject(x) AND IdentityCriteriaSatisfied(x) -> ArtificialObject(x).
Intentional production is evidence, not a universal substitute for context-sensitive classification.

[07] ENTITY CLASSIFICATION
Tool, Instrument, Device, Machine, Structure, Vehicle, Container, Garment, Furniture, InfrastructureComponent, ElectronicDevice, MedicalDevice, Robot, ManufacturedMaterialObject.

[08] CLASSIFICATION AXIOMS
Tool(x) -> ArtificialObject(x).
Machine(x) -> ArtificialObject(x).
Vehicle(x) -> ArtificialObject(x).
ArtificialObject(x) does NOT imply Machine(x).
Digital-only software is an InformationObject, not a physical artificial object; hardware storing it is physical.

[09] IDENTITY AND PERSISTENCE
Identity(x,t1,t2) depends on continuity, component replacement, maintenance history and domain criteria. Material sameness is neither always necessary nor always sufficient for artefact identity.

[10] DESIGN AND SPECIFICATION
HasDesign(x,d); Specifies(d,s); MeetsSpecification(x,s,t).
Design intention, engineering requirement, verified performance and actual use must remain separately typed.

[11] MATERIAL COMPOSITION
MadeOf(x,m) -> MaterialEntity(m).
HasComponent(x,y,t) -> PhysicalEntity(y).
PartOf(y,x,t) may be time-indexed and need not be transitive for all part relations.

[12] STRUCTURAL RELATIONS
ConnectedTo(x,y,t); AttachedTo(x,y,t); SupportedBy(x,y,t); Encloses(x,y,t); LocatedIn(x,r,t).
Spatial topology must be distinguished from functional dependency.

[13] FUNCTIONAL RELATIONS
DesignedFor(x,f) does not imply Performs(x,f,t,c).
Performs(x,f,t,c) does not imply DesignedFor(x,f).
A broken artefact can retain its intended function while failing performance.

[14] LIFECYCLE STATES
Proposed -> Designed -> Fabricated -> Assembled -> Tested -> Deployed -> Maintained -> Retired -> Reused | Recycled | Disposed.
Transitions require evidence and may branch or repeat.

[15] PRODUCTION PROVENANCE
CreatedBy(x,g); ProducedAt(x,t); ProducedUsing(x,p); DerivedFrom(x,y).
Track evidence, provenance and manufacturing batch independently of semantic identity.

[16] TEMPORAL MODEL
State(x,t) : LifecycleState
Property(x,k,t) : Value
Event(e,t) : Event
Change(x,k,t1,t2) -> requires a measured or documented property difference.

[17] MEASUREMENT MODEL
MeasuredProperty(x,q,v,u,t,method) where q is quantity, v numeric value, u unit.
Include measurement uncertainty and calibration status; do not confuse a nominal design value with observation.

[18] GEOMETRIC MODEL
Geometry(x) : SolidRegion | SurfaceMesh | ParametricModel
Volume(x) >= 0; Mass(x) >= 0
BoundingVolume and occupied volume may differ.

[19] MECHANICAL MODEL
For a rigid component, F_net = m*a and tau_net = dL/dt under the appropriate inertial-frame assumptions.
For deformation, use constitutive law sigma = C:epsilon only within linear-elastic regime.

[20] ENERGY AND PERFORMANCE
Efficiency eta = useful_output_energy / input_energy for a defined system boundary and interval, with 0 <= eta <= 1 for conventional energy efficiency.
Function-specific quality metrics require explicit definitions.

[21] RELIABILITY MODEL
R(t)=P(T_failure > t).
For constant hazard lambda >= 0, R(t)=exp(-lambda*t); this is a model assumption, not a universal law.

[22] SAFETY AND FAILURE
FailureMode(x,m); Hazard(x,h); Mitigation(x,k).
Risk evaluation must include context, severity, exposure and uncertainty; absence of recorded failure is not proof of safety.

[23] ENVIRONMENTAL INTERACTION
ConsumesResource(x,r,t); Emits(x,e,t); GeneratesWaste(x,w,t).
Lifecycle assessment must declare functional unit, system boundary and allocation method.

[24] HUMAN INTERACTION
OperatedBy(x,g,t); UsedBy(x,g,t); MaintainedBy(x,g,t).
Distinguish intended users from actual users and authorisation from physical capability.

[25] CONTROL AND AUTOMATION
ControlledBy(x,c,t); Senses(x,s,t); ActuatedBy(x,a,t).
A device may be passive, manually operated or autonomously controlled.

[26] SEMANTIC RELATIONSHIPS
is_a, instance_of, part_of, made_of, designed_for, performs, produced_by, operated_by, located_at, equivalent_to, similar_to, differs_from, supersedes.

[27] SEMANTIC EQUIVALENCE
Equivalent(x,y,context) requires matching identity criteria or explicitly declared conceptual equivalence.
Similarity of purpose or appearance alone does not establish identity.

[28] INFERENCE RULES
R1: HasComponent(x,y,t) -> PhysicalEntity(y).
R2: DesignedFor(x,f) AND NOT Performs(x,f,t,c) -> FunctionalFailureCandidate(x,f,t,c), if performance was actually tested.
R3: MeetsSpecification(x,s,t) -> requires evidence of all mandatory criteria.
R4: Retired(x,t) does not imply Destroyed(x,t).

[29] CONSTRAINTS
C1: no material composition assertion without evidence.
C2: lifecycle states must be time-scoped.
C3: units must be dimensionally valid.
C4: design and observation must not be conflated.
C5: physical and purely informational objects must not be merged.

[30] WORKED EXAMPLE: HAMMER
h: Hammer
ArtificialObject(h)=true
Tool(h)=true
DesignedFor(h,DrivingNails)=true
MadeOf(h,SteelAndWood)=true [example only; inspect actual object]
Performs(h,DrivingNails,t,c) remains unknown until observed.

[31] WORKED EXAMPLE: BRIDGE
b: Bridge
ArtificialObject(b)=true
Structure(b)=true
DesignedFor(b,SupportingTransport)=true
HasComponent(b,Deck,t)=true
MeetsSpecification(b,LoadRating,t) requires inspection and design evidence.

[32] WORKED EXAMPLE: SMARTPHONE
p: Smartphone
ArtificialObject(p)=true
ElectronicDevice(p)=true
HasComponent(p,Battery,t)=true
DesignedFor(p,Communication)=true
Software installed on p is an information artefact distinct from p.

[33] COUNTEREXAMPLES
Naturally formed stone: physical but not necessarily artificial.
A digital CAD drawing: designed information object, not a physical manufactured object.
A naturally occurring tree used as a shelter: use alone does not establish deliberate manufacture.

[34] ONTOLOGY ALIGNMENT
Physical Entity -> Artificial Entity -> Artificial Object
Related: Material Ontology; Mechanical Component; Machine; Sensor; Actuator; Robot; Vehicle; Building; Engineering Systems.

[35] VALIDATION TESTS
V01: every ArtificialObject is a PhysicalEntity.
V02: all components are typed PhysicalEntity.
V03: design intent is distinct from observed function.
V04: measurements include units.
V05: temporal claims include time.
V06: negative claims require appropriate evidence.
V07: lifecycle marker present.

[36] CONFIDENCE
CONFIDENCE.value in [0,1]
CONFIDENCE.basis = direct_measurement | documented_specification | peer_reviewed_source | inference | hypothesis
Never treat an illustrative confidence value as measured certainty.

[37] FLOW HASH
FLOW_HASH.algorithm: SHA-256
FLOW_HASH.scope: canonical UTF-8 content excluding hash field itself
FLOW_HASH.value: compute upon ingestion after canonicalisation; not pre-populated.

[38] UPDATE MODEL
UPDATE_MODEL.version: 1.1
UPDATE_MODEL.trigger: source revision | corrected scientific claim | new validated relation
UPDATE_MODEL.policy: preserve provenance; validate dependencies; increment revision; recompute hash.

[39] AI CONSUMPTION
AI_CONSUMPTION.mode: typed semantic graph + rule-based validation
AI_CONSUMPTION.uses: retrieval, entity resolution, engineering knowledge integration, explainable inference
AI_CONSUMPTION.guardrails: preserve source provenance, uncertainty, temporal scope and domain limitations.

[40] SOURCE AND AUTHORSHIP
SOURCE_URL: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/artificial-entities/artificial-object-ontology.html
SOURCE_STATUS: Live webpage not independently verified; independent formal synthesis.
AUTHORS: Mark Higgins — Sky (Recursive AI)
PUBLISHER: Kireth Research
WEBSITE: https://kirethresearch.org

[41] COMPLETENESS CHECK
All numbered sections present; terminator required.
END_OF_FLOW_ALGEBRA: ArtificialObjectOntology:v1.1