New Relational Planner Achieves Four-Order Speedup in Complex Decision Problems

Global: New Relational Planner Achieves Four-Order Speedup in Complex Decision Problems

A team of computer scientists has unveiled a relational forward planner called Foreplan that dramatically accelerates policy computation for Markov decision processes involving large numbers of indistinguishable objects and concurrent actions. The work, authored by Florian Andreas Marwitz, Tanya Braun, Ralf Möller, and Marcel Gehrke, was first submitted to arXiv on May 28, 2025 and revised on January 28, 2026. According to the abstract, Foreplan delivers speedups of at least four orders of magnitude compared with traditional enumeration methods.

Background and Challenge

Decision‑making problems in artificial intelligence are often modeled as Markov decision processes (MDPs). When the environment contains many objects that cannot be distinguished from one another, the state space grows exponentially, and the action space can expand similarly if multiple actions may occur simultaneously. This combinatorial explosion makes exact policy computation infeasible for most real‑world scenarios.

First‑Order Representation

To address the scalability issue, the authors propose a first‑order, lifted representation that encodes both state and action spaces in polynomial size relative to the number of objects. By abstracting away object identities, the representation preserves essential relational structure while avoiding explicit enumeration of every possible configuration.

Foreplan and Its Approximation

Foreplan leverages the lifted representation to perform forward planning efficiently. The planner systematically explores feasible action sequences, identifying the minimal subset of objects an agent must manipulate to achieve a target task under given constraints. An approximate variant of Foreplan is also introduced, offering even faster computation at the cost of bounded optimality loss.

Theoretical and Empirical Validation

The paper provides a formal analysis of Foreplan’s computational complexity, demonstrating polynomial bounds under reasonable assumptions. Empirical tests on benchmark relational MDPs confirm the theoretical claims, with reported runtime reductions ranging from 10,000‑fold to greater, depending on problem size and concurrency level.

Implications and Future Directions

These results suggest that lifted forward planning could enable scalable decision‑making in domains such as multi‑robot coordination, automated logistics, and complex game AI, where many similar entities interact concurrently. The authors note that extending the approach to partially observable settings and integrating learning‑based components are promising avenues for further 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.