AIMech AI

Online hybrid temporal regression planning

Algorithms AIMech:OnlineHybridTemporalRegressionPlanning DRAFT
An online planning-and-scheduling procedure that plans successive jobs by goal-regression A* over logical conditions, variable bindings, shared temporal constraints and resource allocations, immediately branches on conflicting resource orders, and retains temporal flexibility until queued plans are clamped and released near execution.
Identity Notes & Concept Scope This descriptive identity denotes the common 2004/2005 online hybrid temporal regression planning procedure, with separate source versions and explicit qualifications. OnlinePlanner and PlanJob are nested procedure labels, and PNS in the retrieval filename is not an alias. Current identity context was rebound at clean merged commit 3f75d017b53af1c74368bd17d23d1383e4f56cbd: 812 native and 442 pinned AIO records; all 1,254 record IDs, names, aliases and scoped synonyms participate in the 4,962-field/3,794-normalized-value screen, with no candidate collision. Four ignored- and hidden-inclusive roots returned 16 distinct matching contents and 36 occurrences. Eleven contents reuse exact historical semantic reading; all five new contents and their complete matching source-progress parents were read. The 1,252 historical records are byte-identical, and the two added CoPE record identity/definition/mapping contexts were freshly read as distinct. Historical 86-definition/nine-neighbor reading is attributed to its original receipt; the 15 selected AIO nodes and 68 incident edges were freshly read on the unchanged pin. The postmerge check found all record bytes and complete search match-event multisets identical to the just-read committed snapshot. Root accepts one DRAFT algorithm under existing main911 source responsibility; native creation, validation and saved review are separately recorded operations.

Claims & Grounded Evidence

8 Claims
Mechanism #sequential-job-temporal-flexibility
The common 2004/2005 procedure builds the schedule greedily one job at a time while retaining the selected action plans for previous jobs. A shared temporal constraint database keeps unreleased action times flexible, allowing a later job to start earlier and older actions to shift within their feasible windows while required within-batch completion order is preserved.
Mechanism #four-part-per-job-regression
For each job, the inner A* search regresses from its desired final state using states containing logical literals, variable bindings, a temporal database and resource allocations. A plan is complete when the regressed literals unify with the desired initial state; the chosen bindings and temporal database are then passed back to the outer procedure for planning later jobs.
Mechanism #immediate-temporal-resource-branching
Regressing an applicable action introduces its duration, sequencing and resource-allocation constraints. Potential overlaps for the same resource are resolved immediately by branching over possible relative orders and propagating their temporal constraints; only feasible child states enter the search queue. The 2004 outline says to generate both orderings, while the expanded 2005 description enumerates available placements among existing allocations and combinations across resources.
Mechanism #imminent-release-and-clamping
After planning a job, the outer procedure checks whether a queued plan starts soon. To preserve submission-order release, it releases plans from the oldest unreleased one through that imminent plan, first fixing their time points to earliest feasible times. The papers justify this clamping using a complete algorithm for maintaining allowable temporal windows; outer-loop temporal constraints are added between individual-job searches so those updates do not invalidate search evaluations mid-search.
Mechanism #completion-time-ranking
Search favors the estimated earliest feasible completion time of the current job in the context of earlier jobs, then smaller predicted makespan and then larger already-realized makespan. In the 2005 Figure 7 notation these quantities are t7, t7−t3 and t7−t4, respectively. A relaxed remaining-duration estimate is inserted before the partial plan to tighten its feasible completion bound.
Mechanism #relaxed-achievement-lower-bound
The described heuristic starts from initial and background literals, propagates achievement-time labels through applicable actions while omitting mutex/resource contention, and uses the maximum required-literal achievement time as a remaining-duration lower bound, with infinity if a required literal cannot be produced. Its admissibility interpretation requires the intended minimum relaxed achievement times; the printed first-production description alone does not specify a complete minimum-label agenda or relaxation implementation.
Mechanism #expanded-general-regression-rule
The 2005 general logical representation distinguishes true, false and unknown atoms. Applicability requires effects consistent with the regressed state and preconditions unaffected by those effects also consistent with it. For an applicable action, writing primes for the new sets, the source update is Lt′=(Lt∖Add)∪Pre+, Lf′=(Lf∖Del)∪Pre− and Lu′=(Lu∪Add∪Del)∖(Pre+∪Pre−). These are the expanded paper’s general rules, not its later simplified effect-only rule and not equations printed in the 2004 paper.
Scope #resource-time-noop-relevance
The 2005 discussion does not impose an additional requirement that an applicable action have an effect matching a required goal literal. It explains that a time-consuming no-op may let an existing resource allocation expire and thereby enable a better subsequent plan. This is a source-specific reason for retaining otherwise applicable actions, not a general proof that arbitrary irrelevant actions improve planning.

⚠️ Curated Limitations & Negative Results

Artificial Intelligence Ontology (AIO) Grounding

Explicit AIO Snapshot Assessment: No Match

Rationale: None of the inspected pinned classes identifies or usefully subsumes this symbolic online regression planning/scheduling procedure. Shared timing, regression, graph, model, control or physical application language is insufficient for a mapping.

Search Scope: All pinned record IDs/names/aliases/scoped synonyms participated in the normalized inventory screen; 15 exact plausible AIO CLASS nodes and all 68 incident edges were freshly read for semantic mapping. No full semantic reading of all442 classes is claimed. This is the attributed historical prospective screen at HEAD c222454fe2e2ce2b9be5fd78a01d47e9961d1f26, whose working tree included the three later cohort363 records; it is not a current or future insertion clearance. The 15 selected nodes/68 edges were read by the prior proposal author, not freshly reread as a whole during this draft.
Examined AIO Candidate Rejection Reason
AIO:MachineLearningTask The source specifies symbolic regression search and temporal/resource propagation from supplied models; it does not build a method that learns from training data. Retaining plans or temporal constraints is not a learned predictor.
AIO:Model The plant/action and temporal-network models are inputs/state to an online search-and-release procedure. The whole candidate is not merely an abstract representation of a system.
AIO:MathematicalFunction The complete candidate combines search-state generation, resource-order branching, propagation, queuing and release. A heuristic score is a component, not identity of the entire procedure as a value rule.
AIO:RegressionAnalysis Regression here means reasoning backward from a goal through preceding actions, not statistical estimation of dependent/independent-variable relationships. The pinned class is under SupervisedLearning.
AIO:ReinforcementLearning No reward-based policy/value learning defines this algorithm. Interleaving planning with execution and minimizing completion times does not establish reinforcement learning.
AIO:ReasoningLLM Symbolic logical reasoning in this planner does not use a language model. The related synonym reasoning cannot extend the pinned LLM class to all planning.
AIO:NeuroSymbolicLLM The hybrid refers to planning plus temporal scheduling/constraints, not a neural language model combined with symbolic reasoning.
AIO:EmbodiedLLM Physical manufacturing execution is an application; no grounded multimodal language model is present.
AIO:GraphConvolutionalPolicyNetwork The temporal constraint graph and material routes are not graph convolution or reward/adversarial training of a graph-generating policy.
AIO:PolicyLayer Action and release decisions are not neural-layer predictions of an action probability distribution.
AIO:ProbabilisticGraphicalModel The temporal database expresses constraints between timepoints, not conditional dependence among random variables. A graph representation alone is insufficient.
AIO:DecisionTree Branching over regressed actions/resource orders is search over partial plans rather than the pinned classification/decision-support tree model.
AIO:TemporalBias Temporal constraints and timing objectives are not selection/sampling distortion caused by population changes over time.
AIO:TimeSeriesAnalysis The procedure generates feasible plans/schedules; it is not analysis of observed time series to extract statistics.
AIO:TimeSeriesForecasting Earliest feasible completion estimates from an action/constraint model are not prediction of time-series values from past observations.

Evidence Sources & Primary Literature

2 Sources
Source ID Publication / Document Type / Access Reference & Checksum
ruml2004
Wheeler Ruml, Markus P. J. Fromherz
primary paper
full_text
sha256:b25a8a6bba0e…
ruml2005
Wheeler Ruml, Minh B. Do, Markus P. J. Fromherz
primary paper
full_text
sha256:83126b701cb9…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-16T08:16:24Z
Updated: 2026-10-16T08:16:24Z
Source File: data/curated/OnlineHybridTemporalRegressionPlanning.yaml