Informed NegaScout
Algorithms AIMech:InformedNegaScout DRAFT
An adversarial game-tree search procedure that augments NegaScout’s minimal-window traversal with recursively retained information from an initial search, using remembered inferior left regions and successor upper bounds to guide a later re-search.
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 selects the 1987 complete-information NegaScout procedure with recursive retained bounds and ignore-left/prove-best control. It remains distinct from the separately defined 1985 partial-information policy, baseline NegaScout/PVS, DUAL*, SSS* and memory-controlled ITERSSS*. INS is source-local and is not a global alias. The 1985 source has now been read separately; its correspondence to the 1987 helper/storage/retention descriptions and the other original lineage sources remain tracked. Main911 carries the existing source responsibility while retaining all earlier records and unresolved questions. 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
6 Claims
Mechanism
#adjacent-window-probes
In the displayed integer-score INS procedure, the first successor uses the incoming open window and later initial-search successors use an adjacent-value minimal window around the current bound. A failed-high probe may require an opened-window re-search; the displayed first-successor, fail-low, beta-cutoff, depth-at-most-two and remembered PROVEBEST cases determine when that additional search is avoided.
Mechanism
#ignore-left-information
INS retains the path information identifying the leftmost terminal variation responsible for the initial failure. During a later re-search, previously examined inferior siblings to the left of that variation at even relative levels can be skipped. The source presents ignore-left pruning as a special case of prove-best pruning using remembered extreme merits and successor ordering.
Mechanism
#prove-best-upper-bounds
At odd levels relative to a re-search root, INS retains successor upper bounds from the initial traversal. It uses them to order successors, stop when the larger of the current result a and incoming alpha reaches the next remaining upper bound, and narrow later search windows. Figure3 tests max(a,alpha)>=merit[i+1] and then sets a=max(a,merit[i+1]); the returned result can remain only a fail-low bound. The negamax score orientation and validity of the retained bounds are part of this source-scoped interpretation.
Mechanism
#recursive-information-storage
The complete-information INS presentation saves information recursively throughout the initial search subtree: the relevant successor index at ignore-left nodes and returned successor merits at prove-best nodes. SaveInfo and GetInfo hide its storage management. The reported experiments use a linked tree retaining all this information; the paper separately describes a hash-table option that can lose information through conflicts.
Mechanism
#research-flag-and-unvisited-region
Figure3 distinguishes the invocation’s research flag from a mutable resflag. A re-search invocation retrieves and sorts remembered successor information; when the procedure moves beyond remembered ignore-left information, resflag becomes false for subsequent recursive calls. The displayed SaveInfo call is guarded by research being false, and the procedure returns its current score or bound.
Scope
#reported-comparison-design
The journal compares INS with NS, PNS, SSS* and DUAL* on generated uniform-width, fixed-depth trees with random or specified sibling ordering. The plotted terminal-visit counts are percentages of a minimal game-tree size, averaged over the same twenty sample trees for each displayed setting; they are not wall-clock measurements or universal node-dominance guarantees.
⚠️ Curated Limitations & Negative Results
- The source-scoped procedure and experiments concern finite, fixed-depth alternating game trees, with uniform positive branching in the displays. No general cyclic-graph duplicate policy, stochastic-game semantics, quiescence treatment or arbitrary early-terminal interface is established.
- The adjacent-value window uses integer score units. A failed-window return may be a bound rather than an exact minimax value; arbitrary real-valued epsilon rules, floating-point tolerances and terminal scoring conventions are not specified.
- Figure3 delegates storage to SaveInfo/GetInfo and does not visibly populate merit with each returned v during the initial loop. Array initialization, value capture, cache misses, validity and lifetime therefore remain source-presentation questions rather than a complete executable contract or a proved algorithm defect.
- The displayed pseudocode uses one-based successor indices and reads merit[i+1]. Excluding the i==w case is stated in a comment rather than encoded in that Boolean guard. The listing is not directly executable C and must not be silently repaired.
- A finite hash table can lose remembered information and potential cutoffs; the reported full-information experiment instead retains the generated subtree. Neither establishes a hard total byte bound including the recursion stack, successor arrays, cache policy and allocator overhead.
- Terminal-count curves are conditional on the supplied tree generator, width, depth parity, ordering and twenty shared samples. The paper discusses sampling variability; these results do not prove universal dominance over SSS*, DUAL*, NS, PNS or alpha-beta.
- Preliminary execution-time and OPEN-management observations describe the authors’ implementations, with details deferred to report12. They are not reproduced results, hardware-independent speedups or evidence that fewer terminal visits always means lower time.
- The paper’s PNS stores relevant upper bounds only at the first level, and its footnote discusses NegaScout/PVS lineage. Those descriptions do not by themselves establish PNS as this full INS procedure, global PVS aliases, or equivalence to all later implementations. The 1985 original has been read separately; its version differences and the other cited lineage questions remain separately tracked.
- The defining evidence is the inspected15-page author-hosted1987 journal reproduction. The separate18-page author rendering was inspected only in selected regions, and the TR85-16 route has a cover/title/page-count discrepancy; neither is treated as a wholly equivalent publisher artifact or a fully read independent defining source.
- The draft records the described pruning and recursion mechanism while retaining the abbreviated helper and guard conventions. It does not certify a full correctness or node-dominance theorem, execute INS, or import uninspected proofs from SSS*, NegaScout or alpha-beta references.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No positive target selected after the pinned442identity rows and30full AIO nodes/97incident triples. Generic tree/classification, neural memory, recursion, reinforcement learning and math-operation overlap do not establish a source-backed target relation.
Search Scope: Historical prospective snapshot on 2026-10-16 at 04:51–04:52 UTC, branch goal/discover-ai-concepts-round360, HEAD 3cd85f801666b505b906cedab979423e4f305c40. Compared all 801 maintained and 442 pinned AIO identities (1,243 records; 4,927 identity-field occurrences; 3,766 normalized values). Four fixed-string, case-insensitive, word-delimited searches included ignored and hidden files and excluded .git internals: 10,522 occurrences in 749 distinct contents. Fresh semantic reading covers 747 complete contents and all matching leaves in two large historical wrappers; unrelated wrapper fields were not reread. Read all 30 full candidate AIO nodes and 97 incident triples plus 20 maintained native definitions, identity fields, claim statements and limitations. This historical scope does not clear any future base or assign a source owner.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:MachineLearningTask
|
Its definition requires building methods that learn from data. Retaining computed minimax search bounds is not on its own a source-established machine-learning task. |
AIO:Model
|
A generic abstract representation is not the identity of either operational search algorithm; shared modeling context supplies no informative source-backed mapping. |
AIO:Network
|
A network is an interconnected system. Search-tree nodes alone do not establish a useful algorithm-to-Network mapping or equivalence. |
AIO:ArtificialNeuralNetwork
|
The candidates contain no artificial-neuron architecture or neural training mechanism. |
AIO:DecisionTree
|
The pinned node is a Classification subclass. Its broad decision-support comment does not identify game-tree minimax algorithms as classification procedures. Tree shape or use in choosing moves is insufficient. |
AIO:RandomForest
|
Random Forest is an ensemble of learned trees, not alternative game-tree searches. |
AIO:MathematicalFunction
|
Computing minima/maxima and score bounds does not identify an operational search algorithm with a generic scalar mathematical rule; no specific functional target relation is established. |
AIO:MaximumLayer
|
Elementwise tensor maximum merging differs from alternating-player tree backup and bound manipulation. |
AIO:MinimumLayer
|
Elementwise tensor minimum merging differs from adversarial tree-search control. |
AIO:MemoryCellLayer
|
Storing search information is not a layer of neural memory cells. |
AIO:MemoryAugmentedLLM
|
These algorithms are not language models and retained search bounds do not establish the external-memory LLM architecture. |
AIO:NeuralTuringMachineNetwork
|
Neither source procedure uses a neural Turing-machine network; algorithmic memory alone does not supply this mapping. |
AIO:ReinforcementLearning
|
Searching a supplied game tree with fixed leaf scores and retained bounds is not evidence of reinforcement learning from reward interactions or a policy-learning procedure. |
AIO:ReinforcementLearningLLM
|
Neither procedure is a language model fine-tuned with rewards. |
AIO:TrainingStrategy
|
The source controls search and storage, not a preprocessing/training strategy for machine-learning models. |
AIO:ActiveLearning
|
Information acquisition from a search traversal does not query labels for new training data; INS is not active learning merely because it retains information. |
AIO:ReasoningLLM
|
Neither is an LLM; minimax reasoning does not establish language-task architecture. |
AIO:RecursiveLLM
|
Recursive calls are control flow, not recursive neural language modeling or TreeLSTM composition. |
AIO:RecursiveLanguageModel
|
The source does not define a deep recursive language model or syntactic composition architecture. |
AIO:RecursiveNeuralNetwork
|
Recursion over a game tree does not mean applying learned neural weights to structured inputs. |
AIO:GraphConvolutionalPolicyNetwork
|
Neither method generates goal-directed graphs with a graph convolutional policy network or reinforcement/adversarial training. |
AIO:ProbabilisticGraphicalModel
|
The game tree encodes choices and minimax evaluation, not random-variable conditional dependence. |
AIO:CausalGraphicalModel
|
An acyclic search tree is not automatically a causal probabilistic data-generating graph. |
AIO:Classification
|
Choosing a game move/value is not supervised distribution of observations into classes. |
AIO:HierarchicalClassification
|
A tree hierarchy of moves is not hierarchical classification. |
AIO:HashingLayer
|
INS optional hashing for search-information retention is not categorical feature hashing/bucketing preprocessing. |
AIO:GraphLanguageModel
|
Game-tree representations do not make either procedure a graph language model. |
AIO:NeuroSymbolicLLM
|
Neither combines neural language modeling with symbolic reasoning; symbolic control alone is insufficient. |
AIO:PolicyLayer
|
The procedures do not output a learned neural probability distribution over actions from a layer. |
AIO:GenerativeAdversarialNetwork
|
Minimax/adversarial game terminology does not establish two-neural-network generative training or GAN identity. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
marsland1987
|
T.A. Marsland, Alexander Reinefeld, Jonathan Schaeffer
|
primary paper
full_text
|
ID: doi:10.1016/0004-3702(87)90019-1
sha256:cd73f50f0356…
|
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/InformedNegaScout.yaml