Interface: LtnQueryRequest

@kortexya/reasoninglayer

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@kortexya/reasoninglayer / LtnQueryRequest

Interface: LtnQueryRequest

Defined in: src/types/ltn.ts:155

A tagged-union query over a grounded LTN theory.

Remarks

kind selects the query; the fields each kind needs are validated server-side:

• ground — requires predicate plus either an explicit features vector or feature_text (embedded via the configured embedding backend).
• truth / value — require rules.
• generalisation — requires instances; sort and certainty describe the formula.

Example

const request: LtnQueryRequest = {
  kind: 'truth',
  rules: [{ individual: 'rex', sort: 'dog', membership: 0.9 }],
  aggregator: { kind: 'p_mean_error', p: 2 },
};

Properties

aggregator?

optional aggregator?: LtnAggregator | null

Defined in: src/types/ltn.ts:159

KB-level aggregator (truth, generalisation). Absent ⇒ pMeanError(p = 2).

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certainty?

optional certainty?: number | null

Defined in: src/types/ltn.ts:164

The formula’s certainty (generalisation).

Default Value

1.0

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features?

optional features?: number[] | null

Defined in: src/types/ltn.ts:171

An explicit input feature vector for the queried individual (ground).

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featureText?

optional featureText?: LtnFeatureText[] | null

Defined in: src/types/ltn.ts:169

Ψ-term feature text to embed into the input vector when features is absent and an embedding backend is configured (ground).

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instances?

optional instances?: LtnInstance[] | null

Defined in: src/types/ltn.ts:173

The unseen instances to score (generalisation).

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kind

kind: LtnQueryKind

Defined in: src/types/ltn.ts:157

Which query to answer.

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predicate?

optional predicate?: string | null

Defined in: src/types/ltn.ts:175

The neural predicate name (ground).

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rules?

optional rules?: LtnRule[] | null

Defined in: src/types/ltn.ts:177

The formula’s rules (truth, value).

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sort?

optional sort?: string | null

Defined in: src/types/ltn.ts:179

The formula’s sort, by name (generalisation). Absent ⇒ the root sort.