K-Bounded CoPE scheduling
Algorithms AIMech:KBoundedCoPE DRAFT
An execution-focused CoPE scheduling schema that enumerates placements of a bounded prefix of each candidate action head, completes the remainder with latest-time placement, and compares combined policies obtained from a supplied computation-allocation algorithm.
Identity Notes & Concept Scope
The official K-Bounded_A policy-construction schema that enumerates placements of the first K head actions, combines them with latest-time treatment of remaining head actions, and evaluates reduced instances using supplied A. Retain the explicitly named enumeration/control schema separately from Max-LET. K and the supplied solver A are parameters; K=2 is a reported configuration, not a new concept. No exact K=0 equivalence is asserted from an incomplete printed interface. Truncating the enumerated prefix in general uncertain instances is heuristic. The full bounded-prefix, known-deadline, feasible-schedule result has additional premises. Strictly increasing start indices alone do not explicitly enforce nonoverlap for durations above one. K-Bounded is generic outside this work; retain the CoPE-qualified name and aliases[]. Refined-Max-LET/IPAE remain a version question, not aliases. Current insertion identity was assessed on 2026-10-16 at merged HEAD 8650bfb55ef0c67902b5a80f1527920c87ad1d9d and tree cd6972cabe7b7f1cdf9ca5ee69bd6263a991ba50. The complete inventory contains813 maintained and442 pinned AIO records,1255 total, with no normalized collision among the four candidates. Eight ignored- and hidden-inclusive searches across data/, research/, sources/ and docs/ account for167 distinct matching contents and856 match events. Of these,105 groups retain exact historical scoped-event attribution and62 contents received fresh semantic reading with complete relevant parent context. The newly added manufacturing record was fully compared;22 prior native contexts are byte-identical and retain attributed semantic comparisons. The current mapping assessment read21 complete pinned AIO nodes and72 incident edges. The peer independently checked the current-state work with original scientific authorship disclosed. This is bounded identity clearance for the selected corpus and pin, not universal novelty or source exhaustion. Historical131-content/588-occurrence searching used four whole checkouts and included review/vendor contexts; it is retained by attribution rather than described as the current four-root search. The existing main911 source coordinator is accepted for this insertion without merging method identities or closing source questions.
Claims & Grounded Evidence
4 Claims
Mechanism
#bounded-prefix-placement-enumeration
K-Bounded_A takes a CoPE instance, a prefix parameter K and a supplied S(AE)^2 algorithm A. For each candidate head it starts from the latest-time schedule under the displayed minimum-support induced deadline, then considers placements of the first K actions while retaining latest-time treatment for the remaining head actions. The source explicitly calls this an execution-focused general-case schema.
Mechanism
#reduced-instance-policy-comparison
For each considered head/initiation function, the schema invokes the effective-deadline reduction and supplied algorithm A, combines the returned computation policy with the chosen base initiations, and retains a policy when its returned success probability strictly improves the current best. The fixed-head reduction can still allocate computation to other compatible processes before they become invalid or tardy.
Scope
#exact-special-case-versus-truncation
For known induced deadlines and a constant bound K on the full length of every head, the source describes enumeration of initiation functions followed by allocation optimization as a pseudo-polynomial special case. If a head is longer than the enumerated prefix and the rest is completed by a default scheme, the source explicitly says optimality is given up. Thus the general K-Bounded heuristic with truncated heads is not certified optimal by the bounded-full-head result.
Scope
#parameter-and-procedure-identity
K and solver A parameterize the named placement-enumeration procedure. Its additional initiation search distinguishes it from selecting only latest head placements in Max-LET, while both remain execution-focused schemas. A reported K value or a different supplied solver does not automatically define another algorithm identity, and this record does not identify the official procedure with the version-specific arXiv Refined-Max-LET display.
⚠️ Curated Limitations & Negative Results
- The algorithm is presented for a static supplied-distribution CoPE instance with the source action/model restrictions. It does not supply online estimation, a complete operating planner, arbitrary parallel base actions or stochastic action outcomes.
- Independent performance/deadline calculations require the stated premises. The strict 1−D(t) survival convention and inclusive induced-deadline inequality differ at discrete atoms; the draft does not repair or merge those conventions.
- Algorithm 2 enumerates strictly increasing action-start values but does not explicitly filter duration-dependent nonoverlap. For durations greater than one, increasing starts alone do not ensure legal execution. The intended enumeration must remain scoped to feasible initiation functions; no repaired loop is claimed.
- The display uses I_i[K] and the first K head positions without fully stating short-head, empty-head or out-of-range conventions. Its latest-time helper subtracts H[j] as shorthand for duration and does not fully guard missing support minima or negative schedules. No exact K=0/Max-LET equivalence is inferred from these incomplete interfaces.
- The shared reduction helper lacks an explicit common-prefix-loop increment, second-head-length check and complete all-compatible or empty-head convention. Effective deadlines likewise need mismatch-sentinel and empty-set handling.
- Induced-deadline distributions are absent from the explicit reduction argument list despite being needed by effective deadlines. Ambient access, policy combination, returned probability semantics and all-zero-success fallback remain helper-contract questions. The printed best value begins at0 and changes only on a strict improvement.
- A supplied allocation routine retains its own hazard/logarithm/time-denominator domain restrictions and possible approximation errors. K-Bounded does not establish those numerical conventions or certify its returned success probabilities.
- General initiation enumeration can be exponential. The cited exact results are pseudo-polynomial in numeric horizons and explicit distributions with their known-deadline, feasible-schedule and bounded-head or equal-slack assumptions. Truncating a longer head or using a heuristic A does not inherit exactness; the proof sketches were not formally verified.
- Official Algorithm 2 supplies this version of K-Bounded. The author/main and arXiv artifacts have different displays, and arXiv Refined-Max-LET/IPAE plus the2021 IPAE route remain unresolved lineage. Neither those names nor a particular K/A configuration is accepted as an alias. No author code was acquired or executed.
- The reported evaluation is a configured simulation over sampled15-puzzle-derived profiles and deadlines. It supplies no universal improvement from increasing K, profile calibration guarantee, operational planning overhead bound or statistical-significance result. Rounded small runtimes and instance ellipses retain their stated meanings.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No inspected target identifies or usefully subsumes this named CoPE algorithm schema. A formal model, probability formula, learning task or planner application is not its control identity.
Search Scope: Historical prospective2026-10-16 capture: all 442 pinned AIO IDs/labels/scoped synonyms screened,21relevant full nodes with definitions/annotations and72 distinct incident edges read, with the current native context separately compared. This is not a future canonical-insertion clearance or absence from every ontology.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:BayesianNetwork
|
The target requires a directed acyclic conditional-dependency representation. Competing process outcomes and deadline profiles do not supply that defining representation. |
AIO:CausalGraphicalModel
|
The target concerns assumptions about a data-generating process represented as a probabilistic graph. Action-prefix invalidation is a model transition rule, not evidence of the specified causal-inference model. |
AIO:DecisionTree
|
The selected node is under Classification and its definition frames a classification tree. A policy tree or branching proof construction is not the same classifier, despite broader decision-tool commentary. |
AIO:EmbodiedLLM
|
Executing base-level actions does not make the formal controller or model an embodied large language model. |
AIO:EnsembleLearning
|
Allocating effort among alternative plan computations is not combining learning algorithms to improve prediction. No ensemble learner is specified. |
AIO:GraphConvolutionalPolicyNetwork
|
The target combines graph convolution, reinforcement learning and adversarial graph-generation context. No corresponding architecture or training rule defines these CoPE subjects. |
AIO:MachineLearningTask
|
The target is a process for building methods that learn from data. The formal model and three schemas can consume supplied performance/deadline distributions without learning them. Offline empirical profile estimation in one evaluation does not define these subjects as learning tasks. |
AIO:MarkovChain
|
The pinned node is a stochastic sequence/network context, with Network parent and probabilistic-layer relation. The controlled decision model has action choices and an objective; neither its MDP encoding nor a resulting policy is automatically the pinned chain-network identity. |
AIO:MathematicalFunction
|
The target is a rule assigning a dependent value from independent values. A full decision-model tuple or policy-control procedure is not merely one scalar or function-valued rule. This rejects transfer of the 2025 functional-specific broad_match. |
AIO:MetaLearning
|
The definition concerns learning from metadata about machine-learning experiments. Metareasoning about computation and execution is not that learning mechanism. |
AIO:Model
|
The pinned definition covers an abstract logical, mathematical or conceptual representation of a complex system. The source-defined CoPE decision model is such a representation, so broad_match is defensible for the model only. Named executable-control schemas are not themselves this model; sharing a model input is not class identity. |
AIO:Network
|
A collection of processes and an MDP transition graph do not by themselves identify the subject with the pinned interconnected-node network architecture. Model is the defensible broad target for the formal representation. |
AIO:PolicyLayer
|
The target is a neural-network layer producing action probabilities. The model and supplied-solver policy constructors have no such required layer. |
AIO:ProbabilisticGraphicalModel
|
The target explicitly models conditional dependencies with a graph in a machine-learning task context. The CoPE state/transition representation is not thereby the specified graphical-inference task. |
AIO:ProportionalHazardsModel
|
Multiplicative covariate hazard regression is not a premise or mechanism of the source CoPE model or schedulers. |
AIO:ReasoningLLM
|
These subjects do not require a large language model or its specialized reasoning behavior. |
AIO:ReinforcementLearning
|
The target is a learning task balancing exploration and exploitation to maximize performance. An MDP encoding or a solver-produced policy with supplied distributions does not establish reinforcement learning or a learned controller. |
AIO:SurvivalAnalysis
|
This ML task analyzes duration until events. Deadline/computation distributions are supplied model inputs here; their use does not constitute fitting or evaluating a survival-analysis method. |
AIO:TimeSeriesAnalysis
|
A time clock and completion profiles are not a method for analyzing ordered data series for statistics or characteristics as required by the target task. |
AIO:TrainingStrategy
|
No specified model-training or preprocessing strategy defines the four subjects; estimating evaluation inputs is distinct from their control logic. |
AIO:ActiveLearning
|
The target is interactive querying to label new data points with desired outputs. Allocating computation and dispatching actions for timely planning success is not that task, even though both can value information. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
elboher2023
|
Amihay Elboher, Ava Bensoussan, Erez Karpas, Wheeler Ruml, Shahaf S. Shperberg, Eyal Shimony
|
primary paper
full_text
|
ID: doi:10.1609/aaai.v37i10.26464
sha256:ad81b149dda7…
|
Ontology & Grounding Topology
Open in interactive network graph →Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-16T09:15:13Z
Updated: 2026-10-16T09:15:13Z
Source File:
data/curated/KBoundedCoPE.yaml
· Schema:
curated_concept.yaml
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