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arxiv: 2508.15358 · v2 · submitted 2025-08-21 · 💻 cs.AI

Planning with Minimal Disruption

Alberto Pozanco , Marianela Morales , Daniel Borrajo , Manuela Veloso This is my paper

Pith reviewed 2026-05-18 22:24 UTC · model grok-4.3

classification 💻 cs.AI
keywords plan disruptionplanning compilationsmulti-objective planningAI planningminimal state modificationcost optimizationautomated planning
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The pith

Planning problems can be compiled to find plans minimizing both action costs and changes to the initial state.

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

The paper introduces plan disruption as the extent to which a plan alters the initial state while reaching the goals. It defines several compilations that convert the joint minimization of action costs and this disruption measure into a standard planning task. If the compilations succeed, existing planners can then produce solutions that trade off efficiency against preserving the starting conditions as much as possible. This matters in applications where unnecessary modifications to the environment carry their own costs or risks. The experimental results indicate the reformulated problems remain solvable in practice across multiple benchmarks.

Core claim

The paper formally defines plan disruption and presents planning-based compilations that jointly optimize the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives.

What carries the argument

Planning-based compilations that encode plan disruption as an additive cost to be minimized together with action costs.

If this is right

  • Existing planners can be used directly to produce plans that balance cost and minimal state change.
  • The same compilation approach can be applied across different planning benchmarks without custom solvers.
  • Solutions emerge that achieve goals while keeping the initial state closer to its starting form than cost-only plans would.

Where Pith is reading between the lines

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

  • The method could be adapted to other secondary objectives such as plan length or resource use by changing what the compilation tracks.
  • In dynamic settings the approach might reduce the frequency of replanning by favoring stable initial states.
  • It provides a route to multi-objective planning that reuses single-objective solvers rather than requiring new algorithms.

Load-bearing premise

The proposed compilations correctly encode plan disruption without introducing artifacts that distort the original planning semantics or make the problems unsolvable in practice.

What would settle it

Running a standard planner on one of the compiled problems and finding that the returned plan either fails to achieve the original goals or produces more disruption than a hand-constructed alternative would show the compilation is incorrect.

Figures

Figures reproduced from arXiv: 2508.15358 by Alberto Pozanco, Daniel Borrajo, Manuela Veloso, Marianela Morales.

Figure 1
Figure 1. Figure 1: Rows in the table define the initial state [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives.

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.

Referee Report

2 major / 1 minor

Summary. The paper formally introduces 'plan disruption' as the minimal modification to the initial state required to achieve the goals. It defines several planning-based compilations that reformulate the original task to jointly optimize the sum of action costs and plan disruption. The abstract reports positive experimental results on benchmarks indicating that the reformulated tasks can be solved effectively in practice to generate plans balancing both objectives.

Significance. If the compilations are shown to preserve original semantics without introducing artifacts, this provides a practical method for applications where minimizing initial-state changes is desirable alongside low-cost plans. The use of compilations for joint optimization is a standard technique, and positive experimental results would support its applicability if properly documented with metrics and analysis.

major comments (2)
  1. [Compilations (likely §3–4)] The definitions of the compilations (which introduce auxiliary constructs to track initial-state modifications) do not include an explicit bisimulation, solution-equivalence proof, or argument showing that optimal solutions in the compiled problem correspond exactly to those in the original problem with the intended disruption cost. This is load-bearing for the central claim, as without it the joint optimization risks favoring spurious plans that distort the original transition relation.
  2. [Abstract / Experimental results] The abstract claims positive experimental results on benchmarks showing effective solvability and balanced plans, but provides no details on metrics for disruption, baselines, planning systems used, number of instances, or statistical analysis. This omission prevents verification of the claim that the reformulated task can be solved to balance the objectives.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by briefly indicating the class of benchmarks or the high-level structure of the compilations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and valuable feedback on our manuscript. We address each major comment below, providing clarifications and committing to revisions where appropriate to strengthen the formal arguments and experimental reporting.

read point-by-point responses

  1. Referee: The definitions of the compilations (which introduce auxiliary constructs to track initial-state modifications) do not include an explicit bisimulation, solution-equivalence proof, or argument showing that optimal solutions in the compiled problem correspond exactly to those in the original problem with the intended disruption cost. This is load-bearing for the central claim, as without it the joint optimization risks favoring spurious plans that distort the original transition relation.

    Authors: We agree that an explicit proof is important for rigor. The compilations are constructed such that auxiliary predicates track modifications to the initial state without changing the original transition relation or introducing new actions that could create spurious solutions; the objective function simply adds the disruption cost to the plan cost. However, we acknowledge the absence of a dedicated bisimulation or solution-equivalence theorem in the current draft. We will add a formal proof in Section 3 (or a new subsection) demonstrating that every solution to the compiled problem corresponds to a valid plan in the original problem with the exact intended disruption cost, and vice versa, preserving optimality. revision: yes

  2. Referee: The abstract claims positive experimental results on benchmarks showing effective solvability and balanced plans, but provides no details on metrics for disruption, baselines, planning systems used, number of instances, or statistical analysis. This omission prevents verification of the claim that the reformulated task can be solved to balance the objectives.

    Authors: The full experimental section (Section 5) already reports results across multiple benchmarks, using specific planners, disruption metrics, and instance counts, with comparisons to baselines that optimize only action cost. We concede that the abstract is too terse and omits these details. We will revise the abstract to concisely include key metrics (e.g., average disruption reduction and solvability rates), the planning systems employed, the number of instances, and a brief note on the analysis performed. revision: yes

Circularity Check

0 steps flagged

No circularity: new disruption metric and compilations defined independently

full rationale

The paper formally introduces plan disruption as minimal initial-state modification and defines new planning compilations to jointly optimize action costs plus disruption. These are presented as fresh definitions and reformulations without any reduction to fitted parameters renamed as predictions, self-citation load-bearing premises, or ansatzes smuggled from prior author work. The abstract and structure show an independent formalization followed by experimental validation on benchmarks; no equations or claims collapse by construction to their own inputs. This is the expected non-finding for a paper whose central contribution is a novel metric and encoding rather than a derived result from prior fitted values.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the work builds on standard classical planning formalisms with new objective compilations.

pith-pipeline@v0.9.0 · 5587 in / 888 out tokens · 28761 ms · 2026-05-18T22:24:56.421236+00:00 · methodology

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Reference graph

Works this paper leans on

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