Iterative weakening
Algorithms AIMech:IterativeWeakening DRAFT
A policy for selecting a sufficient search bias by trying a fixed family of search-space restrictions in increasing order of known evaluation complexity, progressively admitting more costly searches until an acceptable result is found.
Identity Notes & Concept Scope
Compared all 442 pinned AIO and 645 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 481, 244, 242 lines. All distinct match contents were inspected. Semantic matching covered 72 AIO and 130 native definitions, with every matched definition read. 11 full annotated AIO node/edge/generated contexts and 5 full native records were inspected. Retain the general policy for ordering a fixed family of search biases by known evaluation complexity. Iterative deepening and broadening are examples along particular dimensions, whereas empirical grid selection, iterated configuration search and statistical racing use different objectives and feedback. Search bias here means an allowed-space restriction, not a social or statistical bias phenomenon.
Claims & Grounded Evidence
5 Claims
Mechanism
#cost-ordered-bias-selection
A bias specifies a restriction on the lower-level search, such as allowed depth, successor breadth or hypothesis complexity. Treat candidate biases as states of a higher-level search and order their evaluation by a supplied complexity function. The inspected analysis fixes the bias family and its evaluation procedure and assumes no useful knowledge is transferred between successive bias trials. It does not learn a cost surrogate from observed trial outcomes.
Scope
#repeated-cost-comparison
The theoretical comparison concerns total search time relative to evaluating a least-cost sufficient bias, rather than solution-path cost or generalization quality. Actual geometric growth in successive trial costs can keep their cumulative cost within a constant factor of the last trial; polynomial growth produces increasing overhead. A literal exponential big-O upper bound alone is insufficient to imply that geometric-growth property.
Mechanism
#multidimensional-multiplicity
For multiple bias dimensions, account for how many states share each cost level as well as how quickly that level grows. The paper studies costs such as b^i+b^j, b^(i+j) and b^(ij). Collapsing evaluation to equal coordinates uses additional componentwise sufficiency information and can be efficient for one cost family yet very costly for another. The printed bound on the number of ordered factor pairs by sqrt(k) needs a constant-factor qualification; that observation alone does not overturn its asymptotic order.
Scope
#joint-breadth-depth-scope
The paper treats joint breadth and depth selection as an application of ordering search costs b^d and proposes grouping by integer logarithmic cost levels. This requires a precise mapping from those levels to integer admissible bounds and componentwise sufficiency. Taking d log2(b) to be exactly an integer would omit many integer-width settings; a practical rounding or domination schedule must be specified. A separate curator construction illustrates one such schedule for widths at least two without asserting that it is the author implementation.
Limitation
#limits-of-policy-guarantees
Ordering evaluation costs does not guarantee a competitive policy for every search space. Slowly growing costs, large equal-cost classes and many bias states sharing the same coordinate description can increase overhead. Benefits from transferred knowledge or instance-dependent success probabilities are outside the fixed no-transfer analysis. The seven-page paper gives theoretical arguments and motivating applications, not a comparative benchmark establishing universal practical speedup.
⚠️ Curated Limitations & Negative Results
- Search bias here denotes an admissible-space restriction, not a statistical or societal-bias identity.
- The policy requires an available ordering of trial evaluation costs and eventual evaluation of a sufficient bias with terminating lower-level searches.
- Asymptotic comparisons depend on cost growth, state multiplicity and additional sufficiency knowledge; they are not guarantees of minimum solution-path cost or statistical generalization.
- Printed big-O, ordered-factor and integer-logarithmic scheduling statements retain the explicit qualifications. Constructed calculations are separate curator reasoning, not author software.
- All seven official conference pages were visually inspected. Separate extended treatment, author implementation and empirical replication remain uninspected.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO counterpart for this general complexity-ordered search-bias policy was established. A deliberate search-space restriction is distinct from the pinned bias phenomena, and optional learning applications do not identify a necessary learning task.
Search Scope: Compared all 442 pinned AIO and 645 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 481, 244, 242 lines. All distinct match contents were inspected. Semantic matching covered 72 AIO and 130 native definitions, with every matched definition read. 11 full annotated AIO node/edge/generated contexts and 5 full native records were inspected. Retain the general policy for ordering a fixed family of search biases by known evaluation complexity. Iterative deepening and broadening are examples along particular dimensions, whereas empirical grid selection, iterated configuration search and statistical racing use different objectives and feedback. Search bias here means an allowed-space restriction, not a social or statistical bias phenomenon.
| 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 a search space is not the computational-bias phenomenon denoted by the pinned class. |
AIO:ModelSelectionBias
|
The policy orders restrictions by evaluation complexity; this is not the statistical bias caused by choosing a model from observed data. |
AIO:TrainingStrategy
|
The method selects restrictions for arbitrary searches. Learning applications do not make training or preprocessing a necessary component. |
AIO:MetaLearning
|
No learned prior from metadata about machine-learning experiments is required by the fixed complexity-order policy. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
provost1993weakening
|
Foster John Provost
|
primary paper
full_text
|
ID: https://cdn.aaai.org/AAAI/1993/AAAI93-112.pdf
sha256:6edac2f4b959…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-13T19:27:41Z
Updated: 2026-10-13T19:27:41Z
Source File:
data/curated/IterativeWeakening.yaml