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
- PNS is described in prose, while Figure 1 lists NS and the appendix lists INS. No complete PNS listing, exact near-root retention depth, principal-variation update rule, bound-storage interface, eviction policy or cache lifetime is supplied. These details must not be filled in by silently copying INS.
- The prove-best explanation depends on valid successor upper bounds and distinguishes those bounds from a true value obtained during re-search. It does not establish that all failed-window returns are exact values or that every complete INS guard and cutoff is retained by PNS. The descriptive PNS policy is not a certified executable implementation or an independently proved optimality theorem.
- The displayed base procedures use integer scores, adjacent-value windows and a tree of supplied width and depth. This draft does not extend PNS to arbitrary real-valued window tolerances, cyclic graphs, transposition handling, stochastic games, quiescence search or unspecified early-terminal conventions.
- The source does not supply a precise PNS space formula or a hard total-memory cap. The partly ambiguous summation on p.1041 concerns retention of all INS information, and the later hash-table suggestion also concerns INS. Neither is a specified PNS storage implementation or a bound covering its entire stack and data structures.
- The 1985 description combines near-root prove-best information with principal-variation retention; the later 1987 journal describes PNS using first-level upper bounds. Their exact implementation, retention-depth and path-management correspondence remains a version question, rather than an established equivalence or demonstrated contradiction.
- The terminal-count comparisons depend on the generated trees, width, depth parity, ordering and twenty-run samples. The paper says further variability measurements are in progress. No curve digitization, runtime reproduction or universal node-dominance claim is made here.
- The reported preferences for PNS and execution-overhead observations describe the authors’ implementations and cost model. They do not establish portable speedups, byte budgets or that fewer terminal visits always yield lower running time; timing claims about INS or SSS* must not be reassigned to PNS.
- The defining artifact is the inspected official-hosted four-page 1985 publication reconstructed by ABBYY in 2006. Extracted text omits listings and parts of the bibliography; all four page images were inspected, but some formula and reference characters remain ambiguous. The seven cited originals were not inspected for this draft and are retained as source questions rather than imported proof evidence.
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