AI-complete¶
An informal label for tasks believed to require capabilities as broad as artificial general intelligence rather than a narrow specialized method.
Core Idea¶
By analogy with completeness in complexity theory, a task is called AI-complete when solving arbitrary real-world instances is thought to entail general language, perception, commonsense, planning, and adaptation, although no accepted formal reduction class exists. The label bundles interdependent capabilities and uses failure of narrow decomposition as evidence that the task can serve as a proxy test for general intelligence. The abstraction is therefore identified by a declared carrier, a transformation or constraint over that carrier, and an invariant that tells an analyst whether the named structure is genuinely present.
Scope of Application¶
AI-complete belongs to artificial intelligence and is useful where the analyst can specify the typed artificial intelligence carrier, defining objects and relations, parameters, conventions, evidence, boundary cases, and comparison targets, then evaluate the claim is explicitly informal, the task scope and success criterion are declared, and no mathematical completeness theorem is inferred without reductions and a formal class. The scope is broad within that domain but bounded by the need for the claim is explicitly informal, the task scope and success criterion are declared, and no mathematical completeness theorem is inferred without reductions and a formal class. The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
Clarity¶
The abstraction clarifies a crowded vocabulary by making the claim is explicitly informal, the task scope and success criterion are declared, and no mathematical completeness theorem is inferred without reductions and a formal class the center of the account. A claim should name the carrier, the governing operation or relation, the applicable assumptions, and the recognition test. A bare label is insufficient because the name AI-complete can be used for a formal identity, an implementation, or a neighboring result unless carrier and convention are stated.
Manages Complexity¶
Without the abstraction, an analyst must reason directly over many local details: the carrier roles, admissibility assumptions, competing conventions, derived invariants, boundary cases, and proof or validation obligations specific to AI-complete. AI-complete compresses them into the roles in the structural signature. That compression permits comparison across instances without erasing the variables that determine validity. It also exposes which details may be varied safely and which are constitutive.
Abstract Reasoning¶
- Identify the carrier. State what the elements, states, objects, or observations are: the typed artificial intelligence carrier, defining objects and relations, parameters, conventions, evidence, boundary cases, and comparison targets. Reject examples whose alleged carrier belongs to a different problem. 2. Lock the constitutive rule. Express the claim is explicitly informal, the task scope and success criterion are declared, and no mathematical completeness theorem is inferred without reductions and a formal class independently of one notation or implementation.
Knowledge Transfer¶
Knowledge transfers strongly among subfields of artificial intelligence because they reuse the typed artificial intelligence carrier, defining objects and relations, parameters, conventions, evidence, boundary cases, and comparison targets, The label bundles interdependent capabilities and uses failure of narrow decomposition as evidence that the task can serve as a proxy test for general intelligence., and type the carrier, state every parameter and convention in the definition, test that the claim is explicitly informal, the task scope and success criterion are declared, and no mathematical completeness theorem is inferred without reductions and a formal class, compare the nearest accepted identity, and report counterexamples, uncertainty, and limiting cases.
Relationships to Other Abstractions¶
Current abstraction AI-complete Domain-specific
Parents (1) — more general patterns this builds on
-
AI-complete is a kind of Complexity Prime
The proposed strict upward parent is
prime:complexity.
Hierarchy path (1) — routes to 1 parentless root
- AI-complete → Complexity
Neighborhood in Abstraction Space¶
AI-complete sits in a crowded region of the domain-specific corpus (20th percentile for distinctiveness): several abstractions share nearly its structure, so a description that fits it tends to fit its neighbors too.
Family — Diagrammatic & Model-Based Reasoning (12 abstractions)
Nearest neighbors
- Constraint satisfaction — 0.93
- Model-based reasoning — 0.92
- Knowledge representation and reasoning — 0.92
- Distributed artificial intelligence — 0.92
- Computational complexity theory — 0.90
Computed from structural-signature embeddings · 2026-09-08