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REVIEW 4 major objections 5 minor 24 references

PDDLFuse: A Tool for Generating Diverse Planning Domains

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read PDDLFuse fuses two PDDL domains into new, solvable planning benchmarks with tunable difficulty.

desk verdict Simple, sound solvability trick, but the published algorithm may never produce valid PDDL because it omits type/parameter handling for added predicates. read the letter →

arxiv 2411.19886 v1 pith:S7BLHLAT submitted 2024-11-29 cs.AI

Vedant Khandelwal , Amit Sheth , Forest Agostinelli This is my paper · ORCID
classification cs.AI
keywords PDDLplanningdomaingenerationrandomizationautomatedfusionplannerevaluationsolvableproblemFastDownward
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

PDDLFuse proposes that the scarcity of diverse planning domains is a bottleneck in planning research, and that existing LLM-based methods only reconstruct known domains from natural language rather than invent new ones. The tool starts from two existing PDDL domains, renames overlapping predicates and actions, and randomly adds or removes preconditions and effects to produce a fused domain with tunable mutation probabilities. Problems are generated by rolling out random applicable actions and setting the goal to a subset of the reached state, which makes every problem solvable by construction. The paper reports that Fast Downward and LPG solve many of these generated instances, with success rates dropping as mutation probabilities, negation, object counts, and fusion depth increase, which the authors interpret as evidence of controllable difficulty. If correct, this gives researchers a parameterized source of novel planning domains for benchmarking planners and training planning models.

What carries the argument

The central object is the domain-fusion mutation procedure (Algorithm 1): take the union of objects, predicates, and actions of two domains after renaming overlaps; for each action, with probability prob_add_pre add predicates to preconditions, with prob_add_eff add effects, with prob_rem_pre and prob_rem_eff remove them, and apply negation with prob_neg. Problems are generated by a random action rollout (Algorithm 3): execute random applicable actions from an initial state for N steps and choose a subset of true predicates as the goal, guaranteeing that a plan exists. This mutation-rollout pair is what carries the claim of novel, solvable, difficulty-tunable domains.

What would settle it

One concrete check: generate a large batch of fused domains and inspect the plans that planners return; if the random mutations are never used in any successful plan and nearly every goal is reachable in one or two steps, the claimed difficulty tuning would be refuted.

Watch

Extended reading notes

Core claim

The central claim is that novel planning domains can be produced by fusing two existing PDDL domains through randomized edits to action preconditions and effects, rather than by translating natural language descriptions of existing domains. The paper argues that this is the planning analogue of domain randomization in reinforcement learning, and that the resulting domains, together with problems generated from random action rollouts, are solvable and diverse enough to stress-test domain-independent planners. The reported experiments show that planner solvability degrades as mutation probabilities, negation probabilities, object counts, and iterative domain depth grow, which the authors take as evidence that PDDLFuse generates complex, adjustable-difficulty domains.

Load-bearing premise

Randomly mutating the union of two PDDL domains yields domains that are semantically meaningful and diverse, not merely syntactically valid and solvable by construction.

Editorial extensions

If this is right

  • Researchers can generate new solvable planning benchmarks without manual domain authoring, using pairs of existing domains as seeds.
  • Tuning the mutation, negation, and object-count parameters shifts planner success rates, giving a way to produce easier or harder test sets.
  • The tool brings the domain-randomization idea from reinforcement learning into PDDL planning, potentially supporting generalization experiments.
  • Generated domains can be used to validate new planners and to probe the limits of domain-independent planners like Fast Downward and LPG.
  • The built-in validator checks PDDL 3.1 syntax and executes plans to confirm goals are reachable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The manuscript refers to the tool as DomGenX in the Background section; this appears to be a naming inconsistency rather than a different method.
  • Diversity is asserted but not measured; a natural extension is to quantify structural distance between generated and seed domains, which the paper does not do.
  • Because problems are solvable by construction, planner success on them may reflect incremental reachability rather than genuine planning difficulty; filtering for non-trivial plans would test this.
  • The depth-level procedure suggests a curriculum-like use of recursively fused domains, but the reported data show declining solvability rather than evidence that deeper domains are meaningfully more complex.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. PDDLFuse is presented as a tool for generating novel, diverse planning domains by fusing two existing PDDL domains. The method first renames overlapping predicates and actions, then unions the objects, predicates, and actions of the two domains, and randomly mutates action preconditions and effects according to tunable probabilities (adding/removing predicates, negation, predicate reversibility). Problem files are generated by executing a random action sequence from an initial state and using a subset of the reached predicates as the goal, which is intended to guarantee solvability by construction. The paper evaluates the resulting domains by running Fast Downward (FF and lmcut heuristics) and LPG on generated instances, reporting success counts across parameter settings and generation depths. The central claim is that PDDLFuse can create complex and varied planning domains that challenge domain-independent planners and support training and evaluation of planning systems.

Significance. If fully established, the contribution would address a genuine bottleneck in planning research: the limited pool of diverse benchmark domains. The solvability-by-construction mechanism is simple and, if implemented correctly, gives a strong guarantee for problem generation. The paper also makes a concrete, falsifiable claim that parameter settings can modulate planner difficulty, which is a useful capability for benchmarking. However, the current evaluation does not demonstrate that the generated domains are either novel or diverse: no diversity metric is defined, no baseline is compared, all experiments use a single random seed, and sample sizes are small and unequal. Moreover, a fundamental technical detail—how predicates added to actions are grounded with typed parameters—is omitted from the specification of the generation algorithm. The significance is therefore conditional on resolving these load-bearing gaps; the tool could be valuable, but the paper as written does not yet substantiate the headline claims.

major comments (4)
  1. [Methods, Algorithm 1 and Background] Algorithm 1 and the formal domain definition omit parameter and type handling, which is load-bearing for PDDL validity. In PDDL, actions are lifted schemas with typed parameters; adding a predicate such as (at ?x ?y) to a precondition or effect requires either reusing parameters of matching types in the action or introducing and declaring new parameters. The paper never specifies this, and the domain tuple (O,P,A) in the Background section contains no type information at all. When fusing domains with different type signatures (e.g., Blocksworld and Gripper), the union operations on lines 1-2 of Algorithm 1 are undefined with respect to how predicates are grounded. The validator is only demonstrated on existing, hand-written domains (Table 2), not on generated domains, so there is no evidence that Algorithm 1 actually produces parseable PDDL. If the implementation silently performs parameter instantiation and type reconciliation, that core mechanism must be described and validated; if it does not, many generated 'domains' would be rejected by a PDDL parser or be semantically ill-formed.
  2. [Results and Conclusion] The paper's central claim of generating 'intricate and varied domains' is not supported by any diversity measurement. No diversity metric is defined or computed, no baseline or comparison to existing domain generation methods is provided, all experiments use a single random seed (seed 42 in System Configuration), and sample sizes are very small (typically 5 problems per configuration, with some rows containing only 1-4 problems due to 'memory constraints' in Solvability Across Parameter Variations and Depth). The heat maps in Figures 1-3 report per-cell counts of solved instances out of at most 5, which is too coarse to establish diversity or to support the parameter-sensitivity conclusions drawn from them. The authors should report quantitative diversity measures over generated domains (e.g., structural or behavioral distances between generated domains), compare against baselines such as mutating a single domain or sampling from existing IPC domains, and use multiple seeds with error bars.
  3. [Problem File Generation and Algorithm 3] The solvability-by-construction guarantee is not tied to the actual generation procedure in the main text, and the paper's own results show a large fraction of unsolved instances. Algorithm 1 line 22 says only 'Execute actions to derive the goal state from the initial state' without specifying whether the action sequence is generated under the mutated domain (as in Algorithm 3) or under the original base domains. If the witness sequence is executed before mutation, the mutations can invalidate it, which would explain why planners often fail on purportedly solvable instances (e.g., Tables 5 and 8 show FD(ff) solving 3/5 and 1/5 at some depths, and LPG frequently solving 0/5). If the witness sequence is generated after mutation, then every problem should be solvable by that witness and the low solver success rates are due only to planner incompleteness; the paper must clarify this point because the interpretation of all experimental tables depends on it. The authors should also state explicitly that Algorithm 3 is the mechanism referenced by Algorithm 1, or integrate the two.
  4. [Background and Related Works (entire repeated section)] The manuscript contains a serious internal inconsistency: the Background and Related Works sections repeatedly refer to a system called 'DomGenX' as the proposed contribution (e.g., 'DomGenX addresses this gap' and 'aligning with DomGenX's goal'), while the title, abstract, and Methods describe 'PDDLFuse'. Moreover, the 'Background and Related Works' section appears twice with overlapping but non-identical text, and the second occurrence introduces DomGenX without any definition. This is not a purely cosmetic issue: the reader cannot determine which tool is being described, and the repeated text suggests a leftover from a previous paper version. The authors must reconcile the terminology and remove the duplicated section before the paper can be evaluated coherently.
minor comments (5)
  1. [System Configuration] The heading 'System Configuraiton' is a typo for 'System Configuration'.
  2. [Domain Generation] In the parameter list, 'revf lag' should be spelled as 'rev_flag' or 'reversibility flag' for consistency with the algorithm notation.
  3. [Figures 1-3] The captions claim the heat maps show 'solvability' but the cells are described in the text as 'the number of solvable instances'; given the maximum of 5 instances per cell, the figures would be much more informative with per-cell success rates and confidence intervals rather than raw counts.
  4. [Experimental Setup and Parameters] The parameter names are inconsistent between the text ('Prob add precond', 'Prob add effect') and Algorithm 1 ('prob_add_pre', 'prob_add_eff'), which makes it difficult to map the reported configurations to the formal notation; a single notation should be used throughout.
  5. [Methods (multiple)] The paper repeatedly refers to 'Supplementary Material' for details of the algorithms, but no supplementary material is provided; this should be included or the descriptions should be made self-contained.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PDDLFuse's generated domains are produced by an explicit mutation procedure and evaluated against external planners; solvability is a by-construction guarantee, not a fitted prediction.

full rationale

The paper's derivation chain is self-contained. Domain generation is an explicit algorithmic procedure (Algorithm 1) that unions objects, predicates, and actions from two base domains and then mutates preconditions and effects with fixed, a priori probabilities. Problem generation executes a random action sequence and selects a subset of true predicates as the goal, so solvability is guaranteed by construction and is presented as a design property, not as a predicted empirical finding. The experimental results measure external planners (Fast Downward, LPG) against the generated problems; these planners are independent benchmarks, and their success rates are observed outcomes rather than quantities derived from the generator's definition. No parameter is fitted to a data subset and then reported as a prediction, and no uniqueness theorem or load-bearing result is imported from the authors' prior work. The two self-citations (Khandelwal, Sheth, and Agostinelli 2024; Agostinelli, Panta, and Khandelwal 2024) appear only in related-work context about generalization of existing models and do not justify the generation mechanism. The internal naming inconsistency between 'PDDLFuse' and 'DomGenX' is a manuscript artifact, not a circular step. The reviewer's concern about missing parameter/type handling for added predicates in Algorithm 1 is a substantive validity/correctness risk, but it does not make the derivation circular: if generated PDDL is malformed, the output fails against an external PDDL standard rather than reducing to its input by definition. Similarly, the diversity claim is unmeasured but that is an evidential gap, not circularity. Accordingly, no circular step can be exhibited.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The generator depends on seven user-chosen probability or count parameters, several domain assumptions about PDDL union validity and goal meaningfulness, and an untested assumption that random mutations preserve semantic coherence. No invented physical entities are introduced.

free parameters (7)
  • prob_add_pre = 0.3, 0.5, 0.7 in experiments
    Probability of adding a new predicate to action preconditions; chosen by hand to modulate generated domain complexity.
  • prob_add_eff = 0.3, 0.5, 0.7 in experiments
    Probability of adding a new effect to actions; chosen by hand.
  • prob_rem_pre = 0.3, 0.5, 0.7 in experiments
    Probability of removing a predicate from preconditions; chosen by hand.
  • prob_rem_eff = 0.3, 0.5, 0.7 in experiments
    Probability of removing a predicate from effects; chosen by hand.
  • prob_neg = 0.3, 0.5, 0.7 in experiments
    Probability of negating added predicates; chosen by hand.
  • num_objs = 5, 10, 15, or None in experiments
    Number of objects in generated problem files; chosen by hand.
  • depth = 1 to 5 in experiments
    Number of iterative fusion generations using previous outputs as bases; chosen as experimental variable.
assumptions (6)
  • domain assumption Union of two PDDL domains after renaming overlapping predicates and actions yields a valid PDDL domain.
    Invoked in Algorithm 1 and the 'Handling Overlapping Names' section; no proof that the union preserves the semantics of either domain.
  • domain assumption Executing a random sequence of actions from an initial state and taking a subset of true predicates as a goal guarantees a meaningful, solvable problem.
    The Action Generator Process (Algorithm 3) relies on this; it guarantees solvability but not that the goal is non-trivial or interesting.
  • domain assumption A PDDL 3.1 parser check is sufficient to establish domain correctness.
    The Validator Process (Algorithm 4) checks syntax and plan execution, not semantic soundness or domain coherence.
  • ad hoc to paper Random mutations of actions preserve enough structure to produce semantically coherent planning domains.
    This is the paper's central modeling choice, but no experiment directly tests whether generated domains are meaningful rather than arbitrary predicate soup.
  • ad hoc to paper Domain randomization success in RL transfers to planning domain generation.
    The paper motivates PDDLFuse with RL domain randomization, but does not establish that the same benefit holds for planning benchmarks.
  • domain assumption Planner solve rates on generated problems are an appropriate measure of domain diversity and complexity.
    The Results section interprets solver success rates as evidence of difficulty and diversity; no independent diversity metric is used.

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Cite this review

Pith. "Pith review of PDDLFuse: A Tool for Generating Diverse Planning Domains." pith.science (2026). https://pith.science/paper/S7BLHLAT

@misc{pith2026241119886,
  author       = {Pith},
  title        = {Pith review of: PDDLFuse: A Tool for Generating Diverse Planning Domains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S7BLHLAT}},
  note         = {Machine review of arXiv:2411.19886}
}
read the original abstract

Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of available domains. While recent advancements have leveraged generative AI technologies such as large language models (LLMs) for domain creation, these efforts have predominantly focused on translating existing domains from natural language descriptions rather than generating novel ones. In contrast, the concept of domain randomization, which has been highly effective in reinforcement learning, enhances performance and generalizability by training on a diverse array of randomized new domains. Inspired by this success, our tool, PDDLFuse, aims to bridge this gap in Planning Domain Definition Language (PDDL). PDDLFuse is designed to generate new, diverse planning domains that can be used to validate new planners or test foundational planning models. We have developed methods to adjust the domain generators parameters to modulate the difficulty of the domains it generates. This adaptability is crucial as existing domain-independent planners often struggle with more complex problems. Initial tests indicate that PDDLFuse efficiently creates intricate and varied domains, representing a significant advancement over traditional domain generation methods and making a contribution towards planning research.

Figures

Figures reproduced from arXiv: 2411.19886 by the authors.

Figure 1
Figure 1. Solvability heat maps for domains with 15 objects, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Solvability heat maps for domains with 15 objects, [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Solvability heat maps for domains with 15 objects, [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

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Reference graph

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