Min-conflicts heuristic
Algorithms AIMech:MinConflicts DRAFT
Aliases: Min-conflicts
A constraint-repair value-selection heuristic that uses a complete assignment to choose a value for a conflicted variable with as few conflicts as possible, breaking tied minimum choices randomly in the inspected formulation.
Identity Notes & Concept Scope
The record identifies the min-conflicts repair/value-ordering heuristic, not one mandatory hill-climbing implementation. The primary report uses it with both local repair and informed backtracking and permits different variable-selection strategies. It does not identify the original GDS network, GENET inhibitory learning, dynamic Breakout weights, or GSAT global best-flip selection. 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 the stated binary-CSP formulation, maintain one domain value for each variable. Select a variable currently in conflict with at least one other variable, and evaluate its possible values against the other current assignments. Choose a minimum-conflict value, breaking ties randomly. The source footnote interprets the binary score as the number of conflicting other variables; for general constraints, estimating how many variables need repair depends on the constraint. The current value remains an eligible candidate, so an update can leave the assignment unchanged or move sideways. Initialization and the choice among conflicted variables belong to the surrounding search strategy. In the described local-repair use, repeat repairs until a consistent assignment is found or a chosen budget ends. The same value-ordering idea can instead guide an informed backtracking procedure; that procedure is not an exact synonym for the heuristic.
⚠️ Curated Limitations & Negative Results
- Minimizing local conflicts does not necessarily reduce distance to a satisfying assignment. Ties, unchanged assignments and non-solution local optima can prevent a local-repair search from terminating successfully.
- Completeness depends on the outer search strategy. The source contrasts a potentially stuck hill-climbing implementation with a complete backtracking strategy; neither a guarantee of eventual success nor a proof of unsatisfiability follows from the value heuristic alone.
- General-constraint scoring requires a declared repair interpretation. Counting violated constraints, distinct conflicting variables, weighted violations or individual forbidden tuples can produce different rankings and should not be silently interchanged.
- Initialization, variable ordering, ties and budgets affect behavior. The source’s simplified probability analysis makes additional problem assumptions and is not asserted as a universal performance guarantee. No numerical benchmark claim 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
|
Steven Minton, Mark D. Johnston, Andrew B. Philips, Philip Laird
|
author technical report
full_text
|
ID: NASA-TM-108121
sha256:4548b61515c4…
|
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/MinConflicts.yaml