Iterative broadening
Algorithms AIMech:IterativeBroadening DRAFT
A tree-search strategy that repeats depth-first search with increasing limits on the number of ordered successors considered at each parent, widening the allowed search tree after an unsuccessful pass.
Identity Notes & Concept Scope
Compared all 442 pinned AIO and 644 native IDs, names, aliases and scoped synonyms; no normalized identity match. Ignored- and hidden-inclusive fixed-string searches of the recovery, older discovery and original Documents checkouts returned 232, 122, 121 lines. All distinct match contents were inspected. Semantic matching covered 72 AIO and 129 native definitions, with every matched definition read. 9 full annotated AIO node/edge/generated contexts and 5 full native records were inspected. Retain repeated depth-first passes with an increasing per-parent successor cutoff. A global depth-layer or frontier beam, repeated depth cutoffs and discrepancy-budget backtracking restrict different portions of the search tree. The1990 conference formulation starts with cutoff2; no alias to every width-adaptive search is asserted.
Claims & Grounded Evidence
5 Claims
Mechanism
#per-parent-breadth-passes
The inspected conference formulation first searches with a successor cutoff of two, then three and so on until a goal is found. Each parent contributes at most that many ordered children during a pass. This bounds local branching rather than the total width of a depth layer or the entire open frontier, and earlier search work may be repeated.
Limitation
#coverage-and-solution-boundaries
Coverage requires each restricted depth-first pass to finish and the cutoff eventually to admit a path to a goal. A finite tree permits this as the cutoff reaches its necessary branching width; a depth-unbounded infinite branch can prevent widening. A first-found goal is not guaranteed minimum path cost. Combining breadth growth with depth limits requires an adequate terminating schedule.
Scope
#special-random-tree-model
The analysis uses a uniform branching factor b and depth d, with exactly s successful children at each successful node, randomly positioned among its b children; the basic derivation takes integer s greater than one. Failure at the next level averages the previous failure probability raised to the number of selected successful children, using a hypergeometric distribution. This correlated family is not an arbitrary tree or a uniform placement of a fixed number of fringe goals.
Limitation
#approximation-and-printed-boundary
The conditional failure ratio for wider after narrower unsuccessful passes requires nested retained child sets and a nonzero conditioning probability. The authors explicitly approximate successful-pass work as unchanged by earlier failure, so the resulting total expected-time calculation is qualified. A cutoff b−s+1 retains a successful child at every successful node in the stated model; later prose prints b+s−1 despite using b−(s−1) in its ensuing sum. The large-depth performance proposition is not asserted here as a universal deterministic speedup.
Limitation
#finite-empirical-boundaries
The random-tree comparisons use finite configured samples and include cases where iterative broadening is slower than ordinary depth-first search. Heuristic ordering changes performance, while the crossword examples combine broadening with other inference and search choices. Those experiments do not isolate a universal runtime improvement, and a binary tree is already fully admitted by the stated initial cutoff of two.
⚠️ Curated Limitations & Negative Results
- This is the1990 conference method; the1992 journal full text is not assumed identical.
- The cutoff is per parent. It is neither a global beam width nor a bound on all program memory.
- Completeness statements require terminating passes and eventual inclusion of an adequate search path. No shortest-path or arbitrary infinite-tree guarantee is asserted.
- The random-tree analysis has specific distributional assumptions and an explicit approximation. Changing child order invalidates its nested-event argument unless nesting is otherwise preserved.
- All five conference pages were visually inspected. Author software and empirical replication were not executed. Constructed examples assess mathematical boundaries only.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable counterpart for the per-parent breadth-widening search policy was established in the inspected AIO pin. Search-tree traversal, learning tasks, learned architectures and computational-bias phenomena have distinct identities.
Search Scope: Compared all 442 pinned AIO and 644 native IDs, names, aliases and scoped synonyms; no normalized identity match. Ignored- and hidden-inclusive fixed-string searches of the recovery, older discovery and original Documents checkouts returned 232, 122, 121 lines. All distinct match contents were inspected. Semantic matching covered 72 AIO and 129 native definitions, with every matched definition read. 9 full annotated AIO node/edge/generated contexts and 5 full native records were inspected. Retain repeated depth-first passes with an increasing per-parent successor cutoff. A global depth-layer or frontier beam, repeated depth cutoffs and discrepancy-budget backtracking restrict different portions of the search tree. The1990 conference formulation starts with cutoff2; no alias to every width-adaptive search is asserted.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:GraphLanguageModel
|
A general tree-search policy need not be a learned graph language model. |
AIO:ReasoningLLM
|
Search ordering does not identify a language-model reasoning architecture. |
AIO:NeuroSymbolicLLM
|
No neural-symbolic language-model architecture is required. |
AIO:MachineLearningTask
|
The method searches a specified tree and need not learn from data. |
AIO:MathematicalFunction
|
Cost and heuristic functions may be evaluated, but the maintained identity is the repeated traversal policy. |
AIO:ReinforcementLearning
|
The search policy does not require reward-based policy learning or the learning-task identity. |
AIO:DecisionTree
|
The pinned classification task is not a traversal of an implicit search tree. |
AIO:ComputationalBias
|
A deliberate restriction of admissible successor choices is not the computational-bias phenomenon denoted by the pinned class. |
AIO:ModelSelectionBias
|
Selecting a search cutoff is not the statistical model-selection bias defined by this class. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
ginsberg1990broadening
|
Matthew L. Ginsberg, William D. Harvey
|
primary paper
full_text
|
ID: https://cdn.aaai.org/AAAI/1990/AAAI90-033.pdf
sha256:d671cd081aea…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-13T19:26:51Z
Updated: 2026-10-13T19:26:51Z
Source File:
data/curated/IterativeBroadening.yaml