AIMech AI

Partially Informed NegaScout

Algorithms AIMech:PartiallyInformedNegaScout DRAFT
A NegaScout variant for adversarial game-tree search that retains prove-best information near the root of each subtree and the principal variation from an initial search to guide a later re-search, as the particular partial-information policy described in the 1985 paper.
Identity Notes & Concept Scope Current prewrite comparison at merged base b5db0b5d88c5fdc5bdc920dedcb26b9b35af6a4f on 2026-10-16 compared 1,246 records (804 native, 442 pinned AIO), 4,937 identity-field occurrences and 3,774 normalized values. Candidate identity collisions were empty; ITERSSS* was the sole normalized context match. Four explicit roots were searched with ignored and hidden files included, using literal source/name probes and separate word-bounded INS/PNS context probes. The 328 distinct complete matching contents were semantically reviewed, and all 602 occurrence references were mechanically reconstructed; the successor collection preserved their exact line multisets. This record denotes the particular 1985 partial-information NegaScout policy retaining near-root prove-best information and the initial principal variation. It is distinct from the complete-information 1987 INS, baseline NegaScout/PVS, DUAL*, all possible partial policies and proof-number search. PNS remains omitted from global aliases. The 1985 near-root-plus-path and 1987 first-level descriptions remain an unresolved version/interface question despite completed reading of both selected primary sources. Main911 carries this separate source responsibility; the proof-number/MCTS and ICAPS04 retrieval questions remain pending. The pinned 30-target AIO no-match assessment is retained with its historical reading scope; current identity clearance is bounded to the inspected corpus and explicit search roots, not global novelty.

Claims & Grounded Evidence

4 Claims
Mechanism #near-root-prove-best-retention
The described PNS version retains information for prove-best cutoffs near the root of each subtree. Section 2 explains the relevant information as successor upper bounds from an initial traversal that examined every descendant without a beta cutoff; Section 3 selects only part of that information for PNS. The paper does not specify a numerical retention depth.
Mechanism #initial-principal-variation
PNS also maintains the principal variation, defined here as the path to the terminal node that the initial search considered best. This identifies the retained path; the paper does not give a separate PNS procedure specifying its later updates.
Scope #particular-partial-information-policy
The authors introduce PNS as a particular compromise between baseline NegaScout and the complete-information INS subtree map. They explicitly allow other possible compromise policies. The PNS description therefore identifies this retention policy rather than every method that saves some NegaScout search information.
Scope #reported-comparison-design
The paper includes PNS in comparisons with AB, NS, INS and SSS* on generated trees with random ordering or a 60% probability that the first descendant is best. Each plotted point averages twenty runs, and terminal-node counts are normalized to minimal game-tree size. Figures 4 and 5 show width-5 and width-20 settings; INS and SSS* results at depths 7 and 8 in Figure 5 are unavailable because of memory constraints. This describes the study design, not a numeric performance guarantee.

⚠️ Curated Limitations & Negative Results

Artificial Intelligence Ontology (AIO) Grounding

Explicit AIO Snapshot Assessment: No Match

Rationale: No suitable positive AIO mapping was identified for this source-specific partial NegaScout procedure. The inspected targets concern learned models, learning tasks, tensor layers or broad mathematical/representational classes. Shared memory, recursion, tree shape, adversarial terminology or game applications alone do not establish an informative source-backed relation. This is a bounded curator assessment of the pinned snapshot, not an upstream ontology assertion.

Search Scope: Historical prospective collection on 2026-10-16 from 05:11:39 to 05:12:47 UTC, branch goal/discover-ai-concepts-round361, HEAD add2c806f6729c8599f4d5cfd6d75c7decde006c. Compared all 1,243 record identities (801 native and 442 pinned AIO; 4,927 field occurrences and 3,766 normalized values). All 18 normalized probes, including the proposed local ID, were clear. Four ignored- and hidden-inclusive fixed-string, case-insensitive, word-delimited searches produced 10 distinct matching contents and 14 occurrences; every content and occurrence binding was read. The complete identity DATA matched the previously read INS/DUAL snapshot exactly. Fresh semantic context covered 114 native identity/definition projections, 12 selected native definitions and all their claim statements, 30 complete annotated AIO nodes and 97 incident triples, and the prospective INS/DUAL drafts. The three actual records added afterward were read in a separate bounded supplement; their addition was reported to bring native counts to 804. No later whole-inventory clearance, global source exhaustion or future ownership is claimed.
Examined AIO Candidate Rejection Reason
AIO:ActiveLearning Acquiring bounds during game-tree search is not interactively querying labels for training data. The 1985 title does not establish the pinned active-learning task.
AIO:ArtificialNeuralNetwork PNS specifies no artificial-neuron architecture or neural training; its stored search information is not a neural network.
AIO:CausalGraphicalModel An alternating game tree is not a causal probabilistic representation of a data-generating process.
AIO:Classification PNS searches values/moves in a supplied game tree; it does not classify observations into learned classes.
AIO:DecisionTree The pinned node is a Classification subclass. Its broader decision-support comment does not identify a partial NegaScout game-tree search algorithm with a classification tree.
AIO:GenerativeAdversarialNetwork Adversarial game-tree evaluation does not imply competing generative/discriminative neural networks or GAN training.
AIO:GraphConvolutionalPolicyNetwork PNS uses neither graph convolutions nor a trained goal-directed graph-generation policy network.
AIO:GraphLanguageModel A game-tree representation is not a graph language model; the source supplies no language-model mechanism.
AIO:HashingLayer Partial search-information storage does not establish categorical feature hashing or a preprocessing layer. The PNS storage contract is itself incompletely specified.
AIO:HierarchicalClassification Successive game moves form a search hierarchy, not a hierarchy of classification labels.
AIO:MachineLearningTask Retaining already computed search bounds is not on its own a task of building methods that learn from data. No such learning task is defined for this PNS.
AIO:MathematicalFunction Generic score arithmetic does not supply an informative relation from the operational partial NegaScout algorithm to the pinned scalar-function class.
AIO:MaximumLayer The pinned operation merges tensors elementwise. Bound comparisons in game-tree re-search do not constitute that layer.
AIO:MemoryAugmentedLLM PNS is not a language model and its retained bounds/principal variation do not establish external-memory LLM identity.
AIO:MemoryCellLayer Selective retention of search information is not a layer made of neural memory cells.
AIO:MinimumLayer The pinned tensor minimum merge is distinct from search-window control and alternating-player value propagation.
AIO:Model The very broad representation class does not identify the source operational procedure or supply a defensible informative mapping merely because a search tree represents a game.
AIO:Network Interconnected search nodes do not make the algorithm itself the pinned generic Network concept; topology alone is insufficient.
AIO:NeuralTuringMachineNetwork The source PNS has no differentiable neural memory controller; using memory does not establish this architecture.
AIO:NeuroSymbolicLLM No neural language model or neural-symbolic integration appears in the selected PNS procedure.
AIO:PolicyLayer The PNS description does not define a neural layer outputting a probability distribution over actions.
AIO:ProbabilisticGraphicalModel Its game tree contains alternatives and score bounds, not a model of random-variable conditional dependencies.
AIO:RandomForest PNS is not an ensemble of learned classification/regression trees. Generated experimental trees are inputs, not trained forest estimators.
AIO:ReasoningLLM Game-tree reasoning does not establish a large language model or the pinned reasoning architecture.
AIO:RecursiveLLM Search recursion is a control convention, not a recursive language-model architecture.
AIO:RecursiveLanguageModel The selected source does not build recursive representations for language modeling.
AIO:RecursiveNeuralNetwork Recursive game-tree traversal does not apply learned neural weights over structured inputs.
AIO:ReinforcementLearning The described finite game-tree search with supplied scores and retained bounds does not learn a policy from reward interactions. A game application alone does not justify even an algorithm-to-task related mapping.
AIO:ReinforcementLearningLLM PNS is not a language model fine-tuned through reinforcement learning.
AIO:TrainingStrategy The source controls search and information retention, not preprocessing or model-training strategy.

Evidence Sources & Primary Literature

1 Source
Source ID Publication / Document Type / Access Reference & Checksum
reinefeld1985
Alexander Reinefeld, Jonathan Schaeffer, T.A. Marsland
primary paper
full_text
sha256:0c1363cd3e91…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-16T06:16:44Z
Updated: 2026-10-16T06:16:44Z
Source File: data/curated/PartiallyInformedNegaScout.yaml