Learning Lifted Action Models from Traces with Minimal Information About Actions and States

Hector Geffner; Jonas G\"osgens; Niklas Jansen

arxiv: 2605.18627 · v1 · pith:Z3ADQN3Inew · submitted 2026-05-18 · 💻 cs.AI

Learning Lifted Action Models from Traces with Minimal Information About Actions and States

Jonas G\"osgens , Niklas Jansen , Hector Geffner This is my paper

Pith reviewed 2026-05-20 10:37 UTC · model grok-4.3

classification 💻 cs.AI

keywords action model learningSTRIPSlifted planningpartial observabilitytrace-based learningSTRIPS+domain acquisition

0 comments

The pith

Lifted STRIPS+ models can be learned from traces with only selected action arguments observed and partial or no state information.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper formulates algorithms and completeness results for learning lifted STRIPS+ action domains from traces that observe full selected action arguments but limited state details. Three cases are handled: no state observability at all, full observability of some predicates, and local observability of some predicates. The results characterize exactly when an equivalent domain can be recovered from such incomplete traces. A sympathetic reader would care because this removes the need for full state snapshots or explicit arguments in every action, making model acquisition feasible from realistic logs and partial sensors.

Core claim

Given a STRIPS+ domain, equivalent lifted models can be learned from traces under three observability regimes, all assuming full observability of selected action arguments: no state observability, full observability of some state predicates, and local observability of some state predicates instead. These results characterize the conditions under which an equivalent domain can be learned from traces.

What carries the argument

Algorithms and completeness proofs for three cases of learning lifted STRIPS+ models from partial traces, where STRIPS+ encodes implicit action arguments in preconditions.

If this is right

Equivalent domains can be recovered even when states are never observed at all, provided the observed action arguments distinguish the actions.
Full observability of some state predicates makes learning possible under weaker requirements on the trace coverage.
Local observability of predicates supports learning when each predicate is checked only against the current state in the trace.
The characterizations give necessary and sufficient conditions on the traces for successful recovery of an equivalent model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same partial-observation approach may apply to real robot execution logs where sensors give only local views rather than complete states.
It suggests testable extensions to traces that also contain occasional noise or missing action arguments.
Connections arise to other settings that learn planning models from demonstrations with incomplete sensor data.

Load-bearing premise

The traces come from a hidden STRIPS+ model where the fully observed action arguments suffice to distinguish the relevant actions.

What would settle it

A concrete STRIPS+ domain together with a collection of traces meeting the observability assumptions for one case, yet from which the algorithms recover a model that differs from the hidden one on some reachable state or action.

Figures

Figures reproduced from arXiv: 2605.18627 by Hector Geffner, Jonas G\"osgens, Niklas Jansen.

**Figure 1.** Figure 1: Dependency graphs for two domains and let P ω ⊆ P be a subset of predicates such that the key predicates in P ω are fully observable, and the others are locally observable. Then, if the dependency graph Gω D formed by assuming that all predicates P ω are fully observable, is acyclic and of rank bounded by dmax, the algorithm LOCAL SYNTH+ will learn a domain equivalent to D. The theorem is a result of the … view at source ↗

read the original abstract

It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequences from a hidden STRIPS model. The result is remarkable because the states are not assumed to be observable at all, and yet it is not practical enough as STRIPS actions include arguments that are not needed for selecting the actions. This shortcoming has been addressed by assuming that the action traces come instead from a hidden STRIPS+ model where some action arguments are implicit in the hidden action preconditions. A limitation of this approach, however, is that it assumes that the states are fully observable. In this work, we relax these restrictions and consider the problem of learning STRIPS+ action domains from traces in a more general context where the traces carry partial information about both actions and states. In particular, we formulate algorithms and completeness results for three general cases, all of which assume full observability of selected action arguments. In the first case, no observability of the state is assumed; in the second case, full observability of some state predicates is assumed, and in the third case, local observability of some state predicates is assumed instead. Given a STRIPS+ domain, these results characterize the conditions under which an equivalent domain can be learned from traces. Experimental results are reported.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Extends trace-based STRIPS+ learning to three partial state observability regimes with algorithms and completeness results, but verification hinges on proof details.

read the letter

This paper extends prior trace-based learning of lifted STRIPS models to STRIPS+ domains under partial state information. It covers three regimes while assuming full observability of selected action arguments: no state observability at all, full observability of some predicates, and local observability of some predicates. The work claims to supply algorithms plus completeness results that characterize when an equivalent domain can be recovered from the traces. Experimental results are mentioned as well. This is a direct and logical next step because it drops the full-state requirement from the recent STRIPS+ work and shows how different amounts of state information change what can be learned. The breakdown into three cases makes the trade-offs clearer than a single general result would. The experiments add some practical grounding even if the abstract keeps the numbers light. The main soft spot is that the provided text gives no algorithm descriptions or proof sketches, so it is hard to check whether the completeness arguments really cover cases where observed arguments and partial states fail to separate distinct lifted actions. If the traces lack separating sequences for implicit arguments, a locally consistent model could still be non-equivalent. The stress-test note on coverage assumptions is reasonable to raise until the full proofs are examined. The citation pattern stays within the relevant planning-learning literature and does not show obvious circularity. This paper is for researchers working on model acquisition in automated planning, especially those handling incomplete observations. A reader focused on theoretical extensions of trace learning will get the most from the new regimes and conditions. It has enough new technical content and formal grounding to deserve a serious referee rather than a desk reject. I would send it for peer review but ask the authors to add clear algorithm outlines and explicit statements on trace coverage assumptions in the next version.

Referee Report

1 major / 1 minor

Summary. The paper claims to formulate algorithms and completeness results for learning equivalent lifted STRIPS+ action models from traces under three observability regimes, all assuming full observability of selected action arguments: (1) no state observability, (2) full observability of some state predicates, and (3) local observability of some state predicates. It characterizes the conditions under which an equivalent domain can be learned from such traces and reports experimental results.

Significance. If the completeness results hold, this extends prior results on learning STRIPS models from action traces alone to more general partial-observability settings for both actions and states. This is potentially significant for practical model acquisition in automated planning, where full state information is rarely available, and the explicit characterization of learnability conditions under minimal information provides a clear theoretical foundation.

major comments (1)

[Completeness results (all three cases)] The completeness results for the three cases do not explicitly state or prove the coverage condition that the given traces contain separating sequences sufficient to distinguish all pairs of distinct lifted actions when some arguments remain implicit in the preconditions. Without this, the reconstruction procedure can return a non-equivalent model that still satisfies local consistency checks, undermining the equivalence guarantee.

minor comments (1)

[Abstract] The abstract states that algorithms, completeness results, and experimental results exist but provides no proof sketches, algorithm descriptions, or quantitative outcomes, which hinders immediate assessment of the contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading of the manuscript and the constructive feedback. We appreciate the acknowledgment of the potential significance of extending completeness results to partial-observability settings. Below we address the major comment directly and indicate the revisions we will make.

read point-by-point responses

Referee: [Completeness results (all three cases)] The completeness results for the three cases do not explicitly state or prove the coverage condition that the given traces contain separating sequences sufficient to distinguish all pairs of distinct lifted actions when some arguments remain implicit in the preconditions. Without this, the reconstruction procedure can return a non-equivalent model that still satisfies local consistency checks, undermining the equivalence guarantee.

Authors: We agree that an explicit coverage condition on separating sequences is necessary to guarantee equivalence when some action arguments are implicit. The current manuscript states that the traces are assumed to be sufficiently informative for the algorithms to recover an equivalent domain, but does not isolate and prove the separating-sequence requirement as a distinct coverage condition for the three observability regimes. We will revise the paper to add a formal definition of separating sequences adapted to the partial-observability cases, prove that the input traces satisfy this condition under the stated assumptions, and show that any model satisfying the local consistency checks is then equivalent to the hidden domain. This addition will appear in the completeness theorems and their proofs for all three cases. revision: yes

Circularity Check

0 steps flagged

No circularity: new algorithms and completeness proofs are self-contained

full rationale

The paper introduces original algorithms and completeness results characterizing conditions for learning equivalent STRIPS+ domains from traces under three partial-observability regimes, all assuming full observability of selected action arguments. These contributions extend prior background results on learning from action traces alone but do not reduce any central claim to a self-definition, a fitted parameter renamed as a prediction, or a load-bearing self-citation whose validity is presupposed without independent support. The derivation chain consists of algorithmic procedures and proof arguments that stand on their own stated assumptions and trace-generation process; no equations or steps equate outputs to inputs by construction. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the standard STRIPS planning formalism and on the existence of a hidden STRIPS+ model that generates the traces; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Traces are generated by some hidden STRIPS+ domain
Stated in the abstract as the source of the input traces.

pith-pipeline@v0.9.0 · 5770 in / 1114 out tokens · 42534 ms · 2026-05-20T10:37:08.866432+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We formulate algorithms and completeness results for three general cases... Given a STRIPS+ domain, these results characterize the conditions under which an equivalent domain can be learned from traces.
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

mutex feature... consistent over a set of extended traces

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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