STRIPS planning method
Algorithms AIMech:STRIPSPlanning DRAFT
A symbolic planning method that uses means–ends analysis and theorem proving over world models to construct a sequence of operators satisfying a goal.
Identity Notes & Concept Scope
This record denotes the planning procedure described in the 1971 STRIPS paper, not a particular executable or every later STRIPS-style action representation.
Claims & Grounded Evidence
1 Claim
Mechanism
#search-procedure
Operators specify preconditions and additions/deletions to a logical world model. The search identifies operators relevant to an unmet goal, treats their preconditions as subgoals, applies an applicable operator and revisits the original goal.
⚠️ Curated Limitations & Negative Results
- The original paper's predicate-calculus world models and theorem-proving procedure should not be equated automatically with every later propositional STRIPS formalism.
- No completeness, runtime or robotic performance guarantee is curated.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO target was identified for this symbolic planning method. AIO's language-model classes share a reasoning topic but do not identify this planner or a verified integration of it.
Search Scope: Compared all 442 pinned AIO labels/scoped synonyms and 56 prior native identities; searched both checkouts including hidden and ignored files. Inspected ReasoningLLM, NeuroSymbolicLLM and MachineLearningTask definitions.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:NeuroSymbolicLLM
|
This class combines neural language modeling with symbolic components; the original STRIPS planning procedure is not such a language model, and no specific integration is asserted. |
AIO:ReasoningLLM
|
The class concerns inference during language tasks, not this operator-based planning procedure. |
AIO:MachineLearningTask
|
The planning procedure searches logical world models; it is not defined as learning methods from data. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
original-paper
|
Richard E. Fikes, Nils J. Nilsson
|
primary paper
full_text
|
ID: doi:10.1016/0004-3702(71)90010-5
sha256:a6813d187806…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:36:55Z
Updated: 2026-10-11T03:36:55Z
Source File:
data/curated/STRIPSPlanning.yaml