Breakout method
Algorithms AIMech:BreakoutMethod DRAFT
Aliases: Breakout
A finite constraint-satisfaction search method that takes strictly improving local assignment changes under a weighted nogood cost and increases the weights of all currently violated nogoods at a non-solution local minimum to change the search landscape.
Identity Notes & Concept Scope
This is Morris’s Breakout method, using weights on forbidden assignment tuples, not the Breakout game. It is distinct from the idealized Fill algorithm, which raises only the current state’s stored cost, and from GLS, which selects features by penalty utility. Shared adaptive-penalty ideas do not make Breakout, GENET and GLS exact synonyms. 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
Represent forbidden assignment tuples as nogoods, give each weight1, and score a complete assignment by the sum of weights of the nogoods it matches. Until a satisfying assignment is reached, make any local assignment change that strictly lowers this weighted cost when one exists. At a non-solution local minimum, increment every currently matched nogood’s weight by1 and repeat the search/weight-increase cycle until an improving move becomes available. Plateau points are treated as local minima rather than traversed by unweighted sideways moves. The method leaves initialization and the choice among improving changes open; the reported implementation uses random initial assignments and the first improving change found in a left-to-right scan. Weight increases persist and can also raise costs of neighboring states sharing the same nogoods.
⚠️ Curated Limitations & Negative Results
- The practical Breakout method is incomplete. The paper supplies a satisfiable four-variable CNF counterexample where repeated reweighting can oscillate without reaching the all-true solution. No finite-budget success or unsatisfiability certificate is implied.
- The paper’s completeness theorem applies to the distinct idealized Fill algorithm, which stores and raises the cost of the current state alone on the finite assignment space. It must not be transferred to Breakout’s shared nogood weights.
- Representation affects the penalty landscape: one constraint can contain several forbidden tuples, and shared nogoods couple costs of different assignments. A constraint-weight implementation requires evidence that its intended representation matches this scope.
- Random initialization is an experimental choice, not a required random restart after each local minimum. Favorable results on constructed distributions do not establish general efficiency. The finite curator calculation checks a prefix of the published counterexample, not an independent proof for infinite executions.
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
|
Paul Morris
|
primary paper
full_text
|
sha256:9f853b683140…
|
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/BreakoutMethod.yaml