GSAT
Algorithms AIMech:GSAT DRAFT
A randomized local-search SAT procedure that repeatedly flips a Boolean variable giving the largest change toward more satisfied CNF clauses, breaks tied choices randomly, and restarts from a random complete assignment under specified flip and attempt budgets.
Identity Notes & Concept Scope
This is the Selman–Levesque–Mitchell GSAT model-finding procedure. It evaluates single-variable flips across the current assignment, rather than first choosing a conflicted variable and then its minimum-conflict domain value. It is not strict hill climbing, a resolution proof procedure, a generic language-model reasoner or every later GSAT/WalkSAT extension. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 310 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in worktree and main, returning 3 and 9 lines respectively. All twelve text hits were inspected: six prior candidate-lead lines across the checkouts and six unrelated Pygments lexer-state lines in the ignored main environment. No hit established an existing candidate record. Seven exact AIO CLASS nodes with scoped synonyms/comments and outgoing edges and five native neighboring mechanisms were inspected. Absence is bounded to this snapshot and semantic neighborhood.
Claims & Grounded Evidence
1 Claim
Mechanism
#method
For up to MAX-TRIES attempts, generate a random complete truth assignment for the input CNF clauses. During each attempt, test the current assignment for satisfaction and, while within MAX-FLIPS, choose a variable whose flip gives the largest increase in the number of satisfied clauses, breaking equally good choices randomly, then flip it. The argmax does not require a positive gain: sideways moves remain possible, and the source also acknowledges occasional decreasing moves. Restart after the per-attempt budget and report search failure after all attempts if no tested satisfying assignment was found. A returned assignment is a directly checkable model; an unsuccessful search does not prove unsatisfiability. Figure1 checks before each flip, so the assignment produced by the last allowed flip is not checked inside its literal loop; a concrete implementation must state whether it adds that final check.
⚠️ Curated Limitations & Negative Results
- GSAT is an incomplete model finder under finite budgets. A satisfiable formula may be missed, and the source discusses families that can require exponentially many attempts. Search failure must not be reported as an UNSAT proof.
- Allowing plateau and occasionally decreasing flips distinguishes the method from strict-improvement hill climbing. Replacing these moves with immediate restarts changes the specified procedure.
- The Figure1 pre-flip test leaves a final-flip boundary: with one allowed flip on a single positive unit clause, an initially false assignment can become satisfying after the last check. The record exposes this literal pseudocode behavior rather than silently certifying a corrected implementation.
- MAX-FLIPS, MAX-TRIES, randomization, tie handling and CNF encoding affect outcomes. Experiments on selected satisfiable instances and particular distributions are not universal runtime or success guarantees; no numerical benchmark result is curated.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable target was found for this symbolic constraint-search mechanism in the inspected snapshot and semantic neighborhood. Broad reasoning, learning or heuristic words do not establish a task, model or bias mapping.
Search Scope: All442 pinned AIO labels/scoped synonyms and310 preceding maintained IDs/names/aliases; ignored/hidden text in both full checkouts; semantic label search for logic, reasoning, satisfiability, constraints, search and heuristics; seven exact AIO CLASS nodes and five complete native neighbors.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:ArtificialNeuralNetwork
|
Historical inspiration by neural CSP solvers does not make these symbolic assignment/weight/flip procedures neural architectures. Min-conflicts was explicitly extracted for symbolic implementations. |
AIO:HopfieldNetwork
|
The method does not require a symmetrically connected content-addressable recurrent neural memory. |
AIO:TrainingStrategy
|
Solving a finite CSP or CNF formula does not require preprocessing or training a machine-learning model. |
AIO:MachineLearningTask
|
A supplied constraint system can be searched without constructing a method that learns from data. General constraint solving is not this learning-task definition. |
AIO:ReasoningLLM
|
The target is a language model that uses reasoning in language tasks, not a general SAT or CSP solver. Its related reasoning/logical-inferences synonyms are not exact identities for these methods. |
AIO:AvailabilityHeuristicBias
|
This is a bias in judgment from overweighting easily recalled information, not a computational heuristic for selecting repairs. |
AIO:MarkovChain
|
A possible stochastic state-space description does not identify the particular repair or weighting method with the pinned network/model class; search can also use deterministic move selection and adaptive memory. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Bart Selman, Hector Levesque, David Mitchell
|
primary paper
full_text
|
sha256:a9715defb5af…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T14:52:25Z
Updated: 2026-10-11T14:52:25Z
Source File:
data/curated/GSAT.yaml