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
- The papers attribute optimality to the search for the current job under its objective and existing-plan context, relying on heuristic admissibility and suitable feasible-state handling. Their greedy outer loop does not establish a globally optimal schedule over future arrivals, a competitive ratio, or optimal revision of earlier action choices.
- The heuristic prose labels a previously unknown literal when produced, but does not give an explicit agenda or label-improvement rule ensuring minimum relaxed achievement times. Admissibility cannot be inferred for an arbitrary first-producer implementation; a fast and a slow producer can require different label handling. This is an exposition/premise limit, not an observed implementation failure.
- Earliest-time clamping is justified only with the source’s appropriate complete temporal-window maintenance and its domain assumptions. It is not a generic guarantee for arbitrary CSPs, approximate or stale windows, or an unspecified propagator. The comparison does not demonstrate that a correctly maintained simple temporal network becomes inconsistent merely by earliest-time clamping.
- The manually chosen imminence interval assumes sufficient time to plan the next job; the papers discuss interrupting and restarting when needed. Outer-loop temporal updates are restricted to between-job searches, while controller changes can abort a current search. These descriptions do not supply a general hard real-time guarantee or fully specified timeout, unreachable-job and empty-frontier behavior.
- The state-uniqueness argument appeals to irreversible logical or resource-order choices reflected in a plan. Local branching distinctions alone do not establish global semantic non-reconvergence, cycle freedom, or a general no-duplicate theorem for arbitrary action domains. No concrete duplicate execution of the authors’ implementation is asserted.
- The 2005 simplified rule tests only whether effects match a fully specified state. Full specification plus corresponding effect preconditions does not alone ensure compatibility of preconditions on unaffected atoms. Such a precondition can contradict the target state even when the simplified effect test passes. The earlier general applicability rule retains the needed check; no implementation defect or author erratum is established.
- The 2005 simplified update flips added/deleted atoms. That update needs opposite-polarity preconditions for the changed atoms and suitable treatment of other preconditions; the phrase corresponding preconditions does not itself specify those polarities. The general regression update is retained separately, and the simplified branch is not silently strengthened or treated as universally equivalent.
- The 2005 paper explicitly supports unknown literals and describes storing true and unknown sets, but later calls the state fully specified and writes a two-set heuristic shorthand. The exact treatment of unknowns in that shorthand remains a source/interface question; this draft does not erase unknowns or claim an implementation-level equivalence of all these descriptions.
- The shared procedure is described for the supplied symbolic manufacturing model: jobs are independent except for shared resources and explicit batch constraints, actions for one job are sequential and abut, and controller delay constrains starting times. These restrictions do not establish applicability to arbitrary interacting, stochastic, partially observable or general durative-planning domains.
- Representational flexibility is not the same as implemented support: the 2004 paper allows duration ranges but says its implementation uses fixed durations, and it describes additional resource-state compatibility restrictions. These details and the 2005 formal extensions remain version-local; this common record does not equate every modeling option or implementation across the two papers.
- The described failure handling assumes reliable transport along planned routes and destination diversion that can discard faulty material; affected jobs can then be replanned from scratch. This does not establish recovery for arbitrary mid-route failures, uncertain state estimation or all in-flight material. Controller-domain changes are handled by aborting the current search under the stated between-search model.
- The 2004 experiments distinguish incremental directed path consistency from an arc-consistency variant maintaining origin-relative time windows without adding arcs. The latter does not provide arbitrary pairwise time relations as easily. The comparison does not prove interchangeability of arbitrary AC-3 and IDPC implementations or transfer the reported timing advantage to every domain.
- Physical-prototype operation, simulations on selected plant configurations and timing/quality comparisons are separate evidence roles. They do not establish universal throughput, hard deadlines or general optimality. This draft makes no numerical performance claim and has not executed an implementation or reproduced the experiments.
- One descriptive identity covers the common outer online coordination and inner temporal/resource regression procedure in these two inspected works, not separately originated A*, STRIPS, STN or propagator components. It does not equate all later printer-planning publications or use the filename token pns as an alias. The 2005 comparison is completed, while remaining cited-original, later-version and implementation questions stay separately tracked.
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