mlsanityData to 14 Oct 2026

Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes

arXiv 1302.1525 ↗First posted 6 Feb 2013cs.AIcited by 24

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Abstract (arXiv)

Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving POMDPs.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Feb 2013.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 7; 2024: 6; 2025: 3.

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  1. 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 86
  2. 2022Learning in Observable POMDPs, without Computationally Intractable Oraclescited by 30
  3. 2022Planning in Observable POMDPs in Quasipolynomial Timecited by 25
  4. 2025LLM-Guided Probabilistic Program Induction for POMDP Model Estimationcited by 12
  5. 2024Agent-state based policies in POMDPs: Beyond belief-state MDPscited by 9
  6. 2024Intrusion Tolerance for Networked Systems through Two-Level Feedback Controlcited by 8
  7. 2023Learning Logic Specifications for Soft Policy Guidance in POMCPcited by 7
  8. 2024Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approachcited by 7
  9. 2024Periodic agent-state based Q-learning for POMDPscited by 6
  10. 2023Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPscited by 5
  11. 2023A Mathematical Characterization of Minimally Sufficient Robot Brainscited by 5
  12. 2025Sequential Monte Carlo for Policy Optimization in Continuous POMDPscited by 4
12 more
  1. 2022Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Modelscited by 3
  2. 2023Intermittently Observable Markov Decision Processescited by 3
  3. 2021Rule-based Shielding for Partially Observable Monte-Carlo Planningcited by 2
  4. 2021Task-Guided Inverse Reinforcement Learning Under Partial Informationcited by 2
  5. 2021Reconciling Rewards with Predictive State Representationscited by 1
  6. 2021Approximation Methods for Partially Observed Markov Decision Processes (POMDPs)cited by 1
  7. 2023Filter-Aware Model-Predictive Controlcited by 1
  8. 2024Online Planning in POMDPs with State-Requestscited by 1
  9. 2024Towards Intention Recognition for Robotic Assistants Through Online POMDP Planningcited by 1
  10. 2025MDPs with a State Sensing Costcited by 1
  11. 2023Fast exploration and learning of latent graphs with aliased observationscited by 0
  12. 2023A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approachcited by 0