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Journal articles on the topic 'Markov Decision Process Planning'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Wang, Lidong, Reed Mosher, Patti Duett, and Terril Falls. "Predictive Modelling of a Honeypot System Based on a Markov Decision Process and a Partially Observable Markov Decision Process." Applied Cybersecurity & Internet Governance 2, no. 2 (2023): 1–5. http://dx.doi.org/10.5604/01.3001.0016.2027.

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A honeypot is used to attract and monitor attacker activities and capture valuable information that can be used to help practice good cybersecurity. Predictive modelling of a honeypot system based on a Markov decision process (MDP) and a partially observable Markov decision process (POMDP) is performed in this paper. Analyses over a finite planning horizon and an infinite planning horizon for a discounted MDP are conducted, respectively. Four methods, including value iteration (VI), policy iteration (PI), linear programming (LP), and Q-learning, are used in analyses over an infinite planning h

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Pinder, Jonathan P. "An Approximation of a Markov Decision Process for Resource Planning." Journal of the Operational Research Society 46, no. 7 (1995): 819. http://dx.doi.org/10.2307/2583966.

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Pinder, Jonathan P. "An Approximation of a Markov Decision Process for Resource Planning." Journal of the Operational Research Society 46, no. 7 (1995): 819–30. http://dx.doi.org/10.1057/jors.1995.115.

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Mouaddib, Abdel-Illah. "Vector-Value Markov Decision Process for multi-objective stochastic path planning." International Journal of Hybrid Intelligent Systems 9, no. 1 (2012): 45–60. http://dx.doi.org/10.3233/his-2012-0146.

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Sarsur, Daniel, Lucas V. R. Alves, and Patrícia N. Pena. "Using Markov Decision Process over Local Modular Supervisors for Planning Problems." IFAC-PapersOnLine 58, no. 1 (2024): 126–31. http://dx.doi.org/10.1016/j.ifacol.2024.07.022.

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Naguleswaran, Sanjeev, and Langford B. White. "Planning without state space explosion: Petri net to Markov decision process." International Transactions in Operational Research 16, no. 2 (2009): 243–55. http://dx.doi.org/10.1111/j.1475-3995.2009.00674.x.

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Schell, Greggory J., Wesley J. Marrero, Mariel S. Lavieri, Jeremy B. Sussman, and Rodney A. Hayward. "Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning." MDM Policy & Practice 1, no. 1 (2016): 238146831667421. http://dx.doi.org/10.1177/2381468316674214.

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Ding, Yi, and Hongyang Zhu. "Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget." Sensors 23, no. 18 (2023): 7846. http://dx.doi.org/10.3390/s23187846.

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Trajectory planning plays a crucial role in ensuring the safe navigation of ships, as it involves complex decision making influenced by various factors. This paper presents a heuristic algorithm, named the Markov decision process Heuristic Algorithm (MHA), for time-optimized avoidance of Unmanned Surface Vehicles (USVs) based on a Risk-Sensitive Markov decision process model. The proposed method utilizes the Risk-Sensitive Markov decision process model to generate a set of states within the USV collision avoidance search space. These states are determined based on the reachable locations and d

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Nguyen, Truong-Huy, David Hsu, Wee-Sun Lee, et al. "CAPIR: Collaborative Action Planning with Intention Recognition." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (2011): 61–66. http://dx.doi.org/10.1609/aiide.v7i1.12425.

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We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments s

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Hai-Feng, Jiu, Chen Yu, Deng Wei, and Pang Shuo. "Underwater chemical plume tracing based on partially observable Markov decision process." International Journal of Advanced Robotic Systems 16, no. 2 (2019): 172988141983187. http://dx.doi.org/10.1177/1729881419831874.

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Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based on partially observable Markov decision process algorithm and artificial potential field algorithm. The partially observable Markov decision process algorithm is used to construct a source likelihood map and update it in real time with environmental information from the sensors on autonomous underwater vehicle in search area. The artificial pot

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Hamasha, Mohammad M., and George Rumbe. "Determining optimal policy for emergency department using Markov decision process." World Journal of Engineering 14, no. 5 (2017): 467–72. http://dx.doi.org/10.1108/wje-12-2016-0148.

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Purpose Emergency departments (ED) are faced with the challenge of capacity planning that caused by the high demand for patients and limited resources. Consequently, inadequate resources lead to increased delays, impacts on the quality of care and increase the health-care costs. Such circumstances necessitate utilizing operational research modules, such as the Markov decision process (MDP) to enable better decision-making. The purpose of this paper is to demonstrate the applicability and usage of MDP on ED. Design/methodology/approach The adoption of MDP provides invaluable insights into syste

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Yordanova, Veronika, Hugh Griffiths, and Stephen Hailes. "Rendezvous planning for multiple autonomous underwater vehicles using a Markov decision process." IET Radar, Sonar & Navigation 11, no. 12 (2017): 1762–69. http://dx.doi.org/10.1049/iet-rsn.2017.0098.

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Lin, Yong, Xingjia Lu, and Fillia Makedon. "Approximate Planning in POMDPs with Weighted Graph Models." International Journal on Artificial Intelligence Tools 24, no. 04 (2015): 1550014. http://dx.doi.org/10.1142/s0218213015500141.

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Markov decision process (MDP) based heuristic algorithms have been considered as simple, fast, but imprecise solutions for partially observable Markov decision processes (POMDPs). The main reason comes from how we approximate belief points. We use weighted graphs to model the state space and the belief space, in order for a detailed analysis of the MDP heuristic algorithm. As a result, we provide the prerequisite conditions to build up a robust belief graph. We further introduce a dynamic mechanism to manage belief space in the belief graph, so as to improve the efficiency and decrease the spa

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Ragi, Shankarachary, and Edwin K. P. Chong. "UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (2014): 2397–412. http://dx.doi.org/10.1109/taes.2014.6619936.

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Ragi, Shankarachary, and Edwin K. P. Chong. "UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (2013): 2397–412. http://dx.doi.org/10.1109/taes.2013.6621824.

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Gedik, Ridvan, Shengfan Zhang, and Chase Rainwater. "Strategic level proton therapy patient admission planning: a Markov decision process modeling approach." Health Care Management Science 20, no. 2 (2016): 286–302. http://dx.doi.org/10.1007/s10729-016-9354-6.

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Mikhalov, Oleksandr Illich, Oleksandr Afrykanovych Stenin, Viktor Petrovych Pasko, Oleksandr Serhiiovych Stenin, and Yurii Opanasovych Tymoshyn. "Situational planning and operational adjustment of the route of the Autonomous robotic underwater vehicle." System technologies 3, no. 122 (2019): 3–11. http://dx.doi.org/10.34185/1562-9945-3-122-2019-01.

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Currently, missions (tasks) for the underwater robot formed using imperative programming methods (both text and graphic), describing in detail the sequence of robot actions that need performed to achieve the desired goal. At the same time, only the operator of the underwater robot, which makes up the mission, for example, the delivery of cargo to the target point, has an idea of the goal itself. Such technology is effective if the robot's mission carried out within a priori scenario. In other cases, it can either not be executed at all, or it can be executed with large violations and a threat

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Bai, Yun, Saeed Babanajad, and Zheyong Bian. "Transportation infrastructure asset management modeling using Markov decision process under epistemic uncertainties." Smart and Resilient Transport 3, no. 3 (2021): 249–65. http://dx.doi.org/10.1108/srt-11-2020-0026.

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Purpose Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure

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Rigter, Marc, Bruno Lacerda, and Nick Hawes. "Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11930–38. http://dx.doi.org/10.1609/aaai.v35i13.17417.

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The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belong to. Minimax regret has been proposed as an objective for planning in UMDPs to find robust policies which are not overly conservative. In this work, we focus on planning for Stochastic Shortest Path (SSP) UMDPs with uncertain cost and transition functions. We introduce a Bellman equation to compute the regret for a policy. We propose a dynamic programming algorithm that utilises the regret Bellman equation, and sho

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OGRYCZAK, WLODZIMIERZ, PATRICE PERNY, and PAUL WENG. "A COMPROMISE PROGRAMMING APPROACH TO MULTIOBJECTIVE MARKOV DECISION PROCESSES." International Journal of Information Technology & Decision Making 12, no. 05 (2013): 1021–53. http://dx.doi.org/10.1142/s0219622013400075.

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A Markov decision process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagent problems in which the value of a decision must be evaluated according to several viewpoints, sometimes conflicting. Although most of the studies concentrate on the determination of the set of Pareto-optimal policies, we focus here on a more specialized problem that concerns the direct determination of policies achieving well-balanced tradeoffs. To this end, we introduce a reference point method based on the optimiz

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Adjei, Patrick, Norman Tasfi, Santiago Gomez-Rosero, and Miriam A. M. Capretz. "Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process." Robotics 13, no. 4 (2024): 63. http://dx.doi.org/10.3390/robotics13040063.

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In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible scenarios. Reinforcement learning has shown promise in robotics due to its superior adaptability over traditional logic. However, the exploratory nature of reinforcement learning can jeopardize safety. This paper addresses the challenges in planning trajectories for robotic arm manipulators in dynamic environments. In addition, this paper highlights

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Cheng, Minghui, and Dan M. Frangopol. "Optimal load rating-based inspection planning of corroded steel girders using Markov decision process." Probabilistic Engineering Mechanics 66 (October 2021): 103160. http://dx.doi.org/10.1016/j.probengmech.2021.103160.

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Bubnov, Yakov. "DNS Data Exfiltration Detection Using Online Planning for POMDP." European Journal of Engineering Research and Science 4, no. 9 (2019): 22–25. http://dx.doi.org/10.24018/ejers.2019.4.9.1500.

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This paper addresses a problem of blocking Domain Name System (DNS) exfiltration in a computer network. DNS exfiltration implies unauthorized transfer of sensitive data from the organization network to the remote adversary. Given detector of data exfiltration in DNS lookup queries this paper proposes an approach to automate query blocking decisions. More precisely, it defines an L-parametric Partially Observable Markov Decision Process (POMDP) formulation to enforce query blocking strategy on each network egress point, where L is a hyper-parameter that defines necessary level of the network se

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Bubnov, Yakov. "DNS Data Exfiltration Detection Using Online Planning for POMDP." European Journal of Engineering and Technology Research 4, no. 9 (2019): 22–25. http://dx.doi.org/10.24018/ejeng.2019.4.9.1500.

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This paper addresses a problem of blocking Domain Name System (DNS) exfiltration in a computer network. DNS exfiltration implies unauthorized transfer of sensitive data from the organization network to the remote adversary. Given detector of data exfiltration in DNS lookup queries this paper proposes an approach to automate query blocking decisions. More precisely, it defines an L-parametric Partially Observable Markov Decision Process (POMDP) formulation to enforce query blocking strategy on each network egress point, where L is a hyper-parameter that defines necessary level of the network se

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Lefebvre, Randy, and Audrey Durand. "On Shallow Planning Under Partial Observability." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26587–95. https://doi.org/10.1609/aaai.v39i25.34860.

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Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (dis- counted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

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Li, Xinchen, Levent Guvenc, and Bilin Aksun-Guvenc. "Autonomous Vehicle Decision-Making with Policy Prediction for Handling a Round Intersection." Electronics 12, no. 22 (2023): 4670. http://dx.doi.org/10.3390/electronics12224670.

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Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging because it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on the decision-making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Pr

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Xu, Jiuyun, Kun Chen, and Stephan Reiff-Marganiec. "Using Markov Decision Process Model with Logic Scoring of Preference Model to Optimize HTN Web Services Composition." International Journal of Web Services Research 8, no. 2 (2011): 53–73. http://dx.doi.org/10.4018/jwsr.2011040103.

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Automatic Web services composition can be achieved using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem, which can lead to a variety of valid solutions. In this paper, the authors propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and find more than one plan, taking i

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de Saporta, Benoîte, Aymar Thierry d’Argenlieu, Régis Sabbadin, and Alice Cleynen. "A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data." PLOS ONE 19, no. 12 (2024): e0315661. https://doi.org/10.1371/journal.pone.0315661.

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Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phas

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Kim, Hongseok, and Do-Nyun Kim. "Maintenance decision-making model for gas turbine engine components." PHM Society European Conference 8, no. 1 (2024): 7. http://dx.doi.org/10.36001/phme.2024.v8i1.4043.

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When designing gas turbine engine components, the inspection and maintenance (I&M) plan is prepared using the safe life. However, the I&M plan determined using safe life may be costly since all components are replaced at designated life. Therefore, it is important to make maintenance decisions considering the time-dependent deterioration process of gas turbine engine components for a cost-saving I&M plan. In this study, we proposed a maintenance decision-making model for gas turbine engine components based on a partially observed Markov decision process (POMDP). Using dynamic Bayes

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Shu, Mingrui, Xiuyu Zheng, Fengguo Li, Kaiyong Wang, and Qiang Li. "Numerical Simulation of Time-Optimal Path Planning for Autonomous Underwater Vehicles Using a Markov Decision Process Method." Applied Sciences 12, no. 6 (2022): 3064. http://dx.doi.org/10.3390/app12063064.

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Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its performance is examined for different oceanic conditions, including complex coastal bathymetry and time-varying ocean currents, revealing advantages compared to the A* algorithm, a traditional path planning method. The ocean current is predicted using a regional ocean model and then provided to the MDP algori

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Monteiro, Neemias Silva, Vinicius Mariano Goncalves, and Carlos Andrey Maia. "Motion Planning of Mobile Robots in Indoor Topological Environments using Partially Observable Markov Decision Process." IEEE Latin America Transactions 19, no. 8 (2021): 1315–24. http://dx.doi.org/10.1109/tla.2021.9475862.

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AlDurgam, Mohammad M. "An Integrated Inventory and Workforce Planning Markov Decision Process Model with a Variable Production Rate." IFAC-PapersOnLine 52, no. 13 (2019): 2792–97. http://dx.doi.org/10.1016/j.ifacol.2019.11.631.

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Kim, M., A. Ghate, and M. H. Phillips. "A Markov decision process approach to temporal modulation of dose fractions in radiation therapy planning." Physics in Medicine and Biology 54, no. 14 (2009): 4455–76. http://dx.doi.org/10.1088/0031-9155/54/14/007.

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Zhang, Zhen, Jianfeng Wu, Yan Zhao, and Ruining Luo. "Research on Distributed Multi-Sensor Cooperative Scheduling Model Based on Partially Observable Markov Decision Process." Sensors 22, no. 8 (2022): 3001. http://dx.doi.org/10.3390/s22083001.

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In the context of distributed defense, multi-sensor networks are required to be able to carry out reasonable planning and scheduling to achieve the purpose of continuous, accurate and rapid target detection. In this paper, a multi-sensor cooperative scheduling model based on the partially observable Markov decision process is proposed. By studying the partially observable Markov decision process and the posterior Cramer–Rao lower bound, a multi-sensor cooperative scheduling model and optimization objective function were established. The improvement of the particle filter algorithm by the beetl

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Yuan, Minsen, Zhenshan Shi, and Zhouyi Shen. "Mission Planning in Time-Invariant Domains with MDPs and Gaussian Distribution." Journal of Physics: Conference Series 2386, no. 1 (2022): 012022. http://dx.doi.org/10.1088/1742-6596/2386/1/012022.

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Abstract This thesis uses the Markov decision process (MDP) to investigate how the optimal path is decided by the underwater robot in the presence of different currents. The use of this MDP decision allows the machine to reduce the unwanted travel energy and travel time in different environments, enhancing the cost of using the machine. The scope of the study is a specific problem, and the paper describes how to use a python planning framework to solve path selection in the case where the direction and strength of the currents are determined, while incorporating a probability distribution of G

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Garbatov, Yordan, and Petar Georgiev. "Markovian Maintenance Planning of Ship Propulsion System Accounting for CII and System Degradation." Energies 17, no. 16 (2024): 4123. http://dx.doi.org/10.3390/en17164123.

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The study’s objective is to create a method to select the best course of maintenance action for each state of ship propulsion system degradation while considering both the present and future costs and associated carbon intensity indicator, CII, rates. The method considers the effects of wind and wave action when considering fouling and ageing. The ship resistance in calm, wave, and wind conditions has been defined using standard operating models, which have also been used to estimate the required engine power, service speed, fuel consumption, generated CO2, CII, and subsequent maintenance cost

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Wang, Kui, Xitao Wu, Shaoyang Shi, et al. "A Novel Integrated Path Planning and Mode Decision Algorithm for Wheel–Leg Vehicles in Unstructured Environment." Sensors 25, no. 9 (2025): 2888. https://doi.org/10.3390/s25092888.

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Human exploration and rescue in unstructured environments including hill terrain and depression terrain are fraught with danger and difficulty, making autonomous vehicles a promising alternative in these areas. In flat terrain, traditional wheeled vehicles demonstrate excellent maneuverability; however, their passability is limited in unstructured terrains due to the constraints of the chassis and drivetrain. Considering the high passability and exploration efficiency, wheel–leg vehicles have garnered increasing attention in recent years. In the automation process of wheel–leg vehicles, planni

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Mubiru, Kizito Paul. "Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand." Brazilian Journal of Operations & Production Management 15, no. 2 (2018): 302–10. http://dx.doi.org/10.14488/bjopm.2018.v15.n2.a12.

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In today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possib

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Bäuerle, Nicole, and Alexander Glauner. "Minimizing spectral risk measures applied to Markov decision processes." Mathematical Methods of Operations Research 94, no. 1 (2021): 35–69. http://dx.doi.org/10.1007/s00186-021-00746-w.

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AbstractWe study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon. The MDP is assumed to have Borel state and action spaces and the cost function may be unbounded above. The optimization problem is split into two minimization problems using an infimum representation for spectral risk measures. We show that the inner minimization problem can be solved as an ordinary MDP on an extended state space and give sufficient conditions under which an optimal policy exists. Regarding the infini

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Morley, C. D., and J. B. Thornes. "A Markov Decision Model for Network Flows*." Geographical Analysis 4, no. 2 (2010): 180–93. http://dx.doi.org/10.1111/j.1538-4632.1972.tb00468.x.

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Isradi, Muhammad, Andri I. Rifai, Joewono Prasetijo, Reni K. Kinasih, and Muhammad I. Setiawan. "Development of Pavement Deterioration Models Using Markov Chain Process." Civil Engineering Journal 10, no. 9 (2024): 2954–65. http://dx.doi.org/10.28991/cej-2024-010-09-012.

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A common phenomenon in developing countries is that the function of the pavement in the road network will experience structural damage before the completion of life is reached, and the uncertainty of pavement damage is difficult to predict. Planning for maintenance treatment depends on the accuracy of predicting future pavement performance and observing current conditions. This study aims to apply the Markovian probability operational research process to develop a decision support system predicting future pavement conditions. Furthermore, it determines policies and effectiveness in managing an

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Larach, Abdelhadi, Cherki Daoui, and Mohamed Baslam. "A Markov Decision Model for Area Coverage in Autonomous Demining Robot." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 2 (2017): 105. http://dx.doi.org/10.11591/ijict.v6i2.pp105-116.

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A review of literature shows that there is a variety of works studying coverage path planning in several autonomous robotic applications. In this work, we propose a new approach using Markov Decision Process to plan an optimum path to reach the general goal of exploring an unknown environment containing buried mines. This approach, called Goals to Goals Area Coverage on-line Algorithm, is based on a decomposition of the state space into smaller regions whose states are considered as goals with the same reward value, the reward value is decremented from one region to another according to the de

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Zhang, Hanrui, Yu Cheng, and Vincent Conitzer. "Planning with Participation Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5260–67. http://dx.doi.org/10.1609/aaai.v36i5.20462.

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We pose and study the problem of planning in Markov decision processes (MDPs), subject to participation constraints as studied in mechanism design. In this problem, a planner must work with a self-interested agent on a given MDP. Each action in the MDP provides an immediate reward to the planner and a (possibly different) reward to the agent. The agent has no control in choosing the actions, but has the option to end the entire process at any time. The goal of the planner is to find a policy that maximizes her cumulative reward, taking into consideration the agent's ability to terminate. We gi

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Zhang, Jian, Mahjoub Dridi, and Abdellah El Moudni. "A Markov decision model with dead ends for operating room planning considering dynamic patient priority." RAIRO - Operations Research 53, no. 5 (2019): 1819–41. http://dx.doi.org/10.1051/ro/2018110.

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This paper addresses an operating room planning problem with surgical demands from both the elective patients and the non-elective ones. A dynamic waiting list is established to prioritize and manage the patients according to their urgency levels and waiting times. In every decision period, sequential decisions are taken by selecting high-priority patients from the waiting list to be scheduled. With consideration of random arrivals of new patients and uncertain surgery durations, the studied problem is formulated as a novel Markov decision process model with dead ends. The objective is to opti

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Winder, John, Stephanie Milani, Matthew Landen, et al. "Planning with Abstract Learned Models While Learning Transferable Subtasks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 9992–10000. http://dx.doi.org/10.1609/aaai.v34i06.6555.

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We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical model

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Bouton, Maxime, Jana Tumova, and Mykel J. Kochenderfer. "Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10061–68. http://dx.doi.org/10.1609/aaai.v34i06.6563.

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Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate

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Kareem, B., and HA Owolabi. "Optimizing Maintenance Planning in the Production Industry Using the Markovian Approach." Journal of Engineering Research [TJER] 9, no. 2 (2012): 46. http://dx.doi.org/10.24200/tjer.vol9iss2pp46-63.

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Maintenance is an essential activity in every manufacturing establishment, as manufacturing effectiveness counts on the functionality of production equipment and machinery in terms of their productivity and operational life. Maintenance cost minimization can be achieved by adopting an appropriate maintenance planning policy. This paper applies the Markovian approach to maintenance planning decision, thereby generating optimal maintenance policy from the identified alternatives over a specified period of time. Markov chains, transition matrices, decision processes, and dynamic programming model

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Walker, Violet, Fernando Vanegas, and Felipe Gonzalez. "Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments." Remote Sensing 15, no. 15 (2023): 3802. http://dx.doi.org/10.3390/rs15153802.

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Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach and software framework for multi-UAV search and target finding within large, complex, and partially observable environments. Mapping and path-solving is carried out by an extended NanoMap library; the global planning problem is defined as a decentralized partially observable Markov decision process and solved using an online model-based solver, and

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Yang, Qiming, Jiancheng Xu, Haibao Tian, and Yong Wu. "Decision Modeling of UAV On-Line Path Planning Based on IMM." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (2018): 323–31. http://dx.doi.org/10.1051/jnwpu/20183620323.

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In order to enhance the capability of tracking targets autonomously of UAV, a model for UAV on-line path planning is established based on the theoretical framework of partially observable markov decision process(POMDP). The elements of the POMDP model are analyzed and described. According to the diversity of the target motion in real world, the law of state transition in POMDP model is described by the method of Interactive Multiple Model(IMM) To adapt to the target maneuvering changes. The action strategy of the UAV is calculated through nominal belief-state optimization(NBO) algorithm which

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Soltan, Sajad, and Maryam Ashrafi. "Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects." Journal of Project Management 10, no. 1 (2025): 71–86. https://doi.org/10.5267/j.jpm.2024.11.001.

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Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response

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