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Depth-first iterative deepening

Algorithms AIMech:DepthFirstIterativeDeepening DRAFT

Aliases: DFID, Iterative deepening depth-first search, IDDFS

A search algorithm that repeatedly performs depth-first searches from the start with successively larger depth limits, redoing shallower work until it reaches a goal.

Identity Notes & Concept Scope This record denotes single-agent depth-bounded iterative deepening, also called iterative deepening depth-first search. It differs from IDA-star, whose limits apply to estimated path cost, and from time-managed minimax iterations in game-playing applications. No normalized identity match was found across the 442 AIO and 119 prior native records; ignored-inclusive text hits in the main checkout were dependency-package documentation and an unrelated package-metadata substring, not existing concept records.

Claims & Grounded Evidence

2 Claims

Mechanism #search-procedure

Complete a depth-limited depth-first search, discard its generated search tree, increase the depth limit, and restart from the initial state. The procedure repeats shallow expansions while retaining only the current depth-first traversal state rather than the complete breadth-first frontier.

supports Source: Depth-First Iterative-Deepening: An Optimal Admissible Tree Search · Locator: Section 4, printed pp. 100–102 / PDF pp. 4–6; procedure and exponential-tree analysis

Mechanism #depth-threshold-distinction

The later primary paper identifies depth-first iterative deepening by a depth-based cutoff, in contrast to IDA-star’s g+h cutoff, and describes repeated depth-first passes that keep the current search path.

supports Source: Linear-Space Best-First Search: Summary of Results · Locator: Previous Work: Iterative Deepening, printed p. 533 / PDF p. 1

⚠️ Curated Limitations & Negative Results

Depth measures operator count, not arbitrary accumulated edge cost. Finite branching is needed for each bounded iteration to finish.
Repeated shallow work remains real. The paper’s exponential-tree analysis is conditional and does not establish uniform efficiency on graphs with many duplicate paths or near-unit branching.
The 1986 erratum to the original journal paper was identified but its text could not be accessed. The procedure is corroborated by the 1992 primary paper; this record does not assert the original global time-and-space optimality theorem.

Artificial Intelligence Ontology (AIO) Grounding

Explicit AIO Snapshot Assessment: No Match

Rationale: No suitable AIO counterpart for this graph/state-space search procedure was identified in the inspected snapshot. Using graphs, evaluation functions or a search subroutine in an AI system does not equate the algorithm with a learned model, a learning task or a mathematical function.

Search Scope: All 442 pinned AIO labels and scoped synonyms and 119 preceding native identities were compared. Both updated checkouts were searched including hidden and ignored files. Exact CLASS nodes and edges for GraphLanguageModel, ReasoningLLM, NeuroSymbolicLLM, MachineLearningTask, MathematicalFunction and ReinforcementLearning were inspected, alongside semantic search for search, planning, heuristic, path, traversal, symbolic, reasoning and graph terminology.

Examined AIO Candidate	Rejection Reason
`AIO:GraphLanguageModel`	A language model operating on structured graph representations is not a graph traversal or shortest-path algorithm.
`AIO:ReasoningLLM`	Language-task deductive inference does not denote general state-space search.
`AIO:NeuroSymbolicLLM`	A language-model architecture combining neural and symbolic components is not the search procedure itself.
`AIO:MachineLearningTask`	Building methods that learn from data is not the identity of an algorithm traversing a specified graph.
`AIO:MathematicalFunction`	The search procedure is distinct from the mathematical functions it may evaluate.
`AIO:ReinforcementLearning`	A learning task balancing exploration and exploitation is distinct from deterministic graph/state-space traversal.

Evidence Sources & Primary Literature

2 Sources

Source ID	Publication / Document	Type / Access	Reference & Checksum
`method-source`	Depth-First Iterative-Deepening: An Optimal Admissible Tree Search Richard E. Korf	primary paper full_text	ID: doi:10.1016/0004-3702(85)90084-0 sha256:44fc92b6885a…
`later-primary-paper`	Linear-Space Best-First Search: Summary of Results Richard E. Korf	primary paper full_text	sha256:f30adb0c2776…

Curation Provenance

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-11T05:32:15Z

Updated: 2026-10-11T05:32:31Z

Source File: data/curated/DepthFirstIterativeDeepening.yaml