Demand-Execution CoPE scheduling
Algorithms AIMech:DemandExecutionCoPE DRAFT
A computation-focused CoPE next-action schema that consults a supplied computation allocator and substitutes a required head action when its latest-start timeliness test demands execution, otherwise returning the recommended computation action.
Identity Notes & Concept Scope
The official computation-focused Demand-Execution_A next-action schema: consult a supplied computation allocator, then initiate a required next head action when the timeliness condition demands execution. Its stepwise action recommendation and computation-first control differ from the execution-focused complete-policy constructors. Retain the named schema; solver A and its configured policies are parameters. The printed W[S] index versus remaining-time meaning, availability of the next head action and other literal interface conditions remain unresolved. Do not treat the display as a certified implementation or import a general optimality theorem. Use the CoPE-qualified descriptive name, aliases[]. No global identity is inferred for the generic phrase demand execution. 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
3 Claims
Mechanism
#computation-first-recommendation
Demand-Execution_A first uses a supplied S(AE)^2 algorithm A to choose the next process computation under the assumption that no base-level actions are executed before computation terminates. The source then tests whether a base action is required to keep that recommended process non-tardy, rather than first fixing and comparing complete head-initiation policies.
Mechanism
#timeliness-triggered-base-action
For the recommended process, the displayed rule selects the minimum support value of its induced-deadline distribution and constructs latest head-start times. It compares a selected head action’s scheduled start to the current wall time and returns that base action when the equality test holds; otherwise it returns the recommended computation action. The identity concerns this timeliness-triggered choice, while the printed head-index convention is retained as an unresolved interface limitation.
Scope
#next-action-schema-output
The official display returns one base-level or computation action for the current state. It is a parameterized next-action selector within CoPE, unlike the execution-focused schemas which construct and compare combined policies. It neither defines the supplied allocator A nor supplies a general optimality theorem, a learned policy or the later2025dual-open-list planner.
⚠️ Curated Limitations & Negative Results
- The selector operates in the source static CoPE abstraction with supplied distributions and sequential non-preemptible base actions. It does not provide online profile learning, full planner integration, arbitrary parallel-action execution or a stochastic physical-action model.
- The supplied allocator and timeliness tests retain their probability premises. Independence is an analysis assumption, and strict deadline survival from1−D(t) differs from the inclusive induced-deadline derivation at atoms. An explicit boundary convention is required before executable interpretation.
- Algorithm 5 assigns index=W[S], although the MDP defines W as remaining duration of the currently executing base action and L as the number of initiated/completed actions. The arXiv display retains the same issue. The draft does not substitute L or another index and does not diagnose the linked implementation.
- The display does not fully specify eligibility when an action is already executing, absent/empty head positions, a completed head, exhausted policy output or no feasible process. The model’s legal-action rules still apply. Latest-time scheduling uses duration shorthand and requires support-minimum and nonnegative/feasible-start conventions.
- The arXiv earliest-time/timeliness transition expression can add W and a computation unit that overlap under its own progress updates. The retained W=1, one-unit remaining computation and empty unstarted-head case gives completion atT+1 in the updates versusT+2 in the expression. This limits a literal interpretation of model-based tardiness without asserting that Demand-Execution code has that behavior.
- The allocation subroutine must declare its state/probability/policy-return contract. Conditional hazard denominators need unfinished positive survival, log-failure utilities need zero/one boundary treatment and LPF/time scores need positive time. None is certified by the wrapper’s call to A.
- Demand-Execution is one of the source’s general-case suboptimal schemes. KID-CoPE structural existence results and pseudo-polynomial exact cases concern different restricted conditions and do not prove this computation-first rule optimal or complete. The arXiv proofs were read without formal verification.
- Official Algorithm 5 and the arXiv older-naming display remain version-specific evidence, with unresolved interface correspondence. Refined-Max-LET/IPAE and the2021 route are not accepted aliases of this selector or of CoPE generally. The author main PDF lacks the referenced appendix; no code was downloaded or run.
- Configured simulations over15-puzzle-derived profiles, chosen solvers and action durations do not establish calibrated success estimates, universal runtime advantage, statistical significance or practical online-planning overhead. Instance ellipses and rounded small runtimes are not confidence intervals or exact measurements.
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
2 Sources| 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…
|
elboher2023-arxiv
|
Amihay Elboher, Ava Bensoussan, Erez Karpas, Wheeler Ruml, Shahaf S. Shperberg, Solomon E. Shimony
|
primary paper
full_text
|
ID: arxiv:2303.02664v1
sha256:1e4af5c0e79f…
|
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/DemandExecutionCoPE.yaml
· Schema:
curated_concept.yaml
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