New Heuristics Enable Numeric Planning with Infinite Action Spaces

Global: New Heuristics Enable Numeric Planning with Infinite Action Spaces

A team of researchers led by Ángel Aso-Mollar, along with Diego Aineto, Enrico Scala, and Eva Onaindia, submitted a preprint on December 26, 2025, that proposes an optimistic compilation technique for numeric planning problems that involve an unbounded set of possible actions. The work, posted on arXiv, aims to extend existing heuristic methods so they can operate effectively in environments where action parameters are free numeric variables.

Background

Numeric planning with control parameters augments the traditional numeric planning model by allowing actions to contain variables that must be instantiated during the planning process. This flexibility creates a potentially infinite action space, complicating the application of conventional numeric heuristics that rely on a finite set of actions.

Problem Statement

Because standard heuristics depend on enumerating or reasoning over the explicit action structure, they become infeasible when faced with an infinite number of applicable actions. Researchers therefore identified a need for a method that can preserve heuristic guidance without exhaustively exploring every possible action instance.

Proposed Approach

The authors define a tractable subclass of problems, termed “controllable, simple numeric problems,” and introduce an optimistic compilation process. This process abstracts control‑dependent expressions into bounded constant effects and relaxed preconditions, effectively converting the original problem into a simple numeric task that existing subgoaling heuristics can evaluate.

Experimental Findings

Using the compiled representation, the study demonstrates that subgoaling heuristics can estimate goal distances with comparable accuracy to traditional methods, while remaining computationally feasible. The results suggest that the approach successfully bridges the gap between infinite‑action settings and established heuristic frameworks.

Implications

By enabling the use of traditional numeric heuristics in previously intractable domains, the technique expands the practical applicability of planning algorithms in areas such as robotics, automated scheduling, and complex decision‑making systems. The authors argue that this advancement pushes the boundaries of the current state of the art in numeric planning research.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.