Relevant bibliographies by topics / Partially Observable Markov Decision Processes (POMDPs)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Partially Observable Markov Decision Processes (POMDPs)'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Partially Observable Markov Decision Processes (POMDPs)"

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NI, YAODONG, and ZHI-QIANG LIU. "BOUNDED-PARAMETER PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES: FRAMEWORK AND ALGORITHM." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 06 (2013): 821–63. http://dx.doi.org/10.1142/s0218488513500396.

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Partially observable Markov decision processes (POMDPs) are powerful for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model the real-life situation precisely, due to various reasons such as limited data for learning the model, inability of exact POMDPs to model dynamic situations, etc. In this paper, assuming that the parameters of POMDPs are imprecise but bounded, we formulate the framework of bounded-parameter partially observable Markov decision processes (BPOMDPs). A modified value iteration is proposed as a basic strategy for ta
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Tennenholtz, Guy, Uri Shalit, and Shie Mannor. "Off-Policy Evaluation in Partially Observable Environments." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10276–83. http://dx.doi.org/10.1609/aaai.v34i06.6590.

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This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is inherently prone to bias, with risk of arbitrarily large errors. We define the problem of off-policy evaluation for Partially Observable Markov Decision Processes (POMDPs) and establish what we believe is the first off-policy evaluation result for POMDPs. In addition, we formulate a model in which observed and unobserved variables are decoupled into two dynamic processes, called a Decoupled POMDP. We show how off-poli
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Carr, Steven, Nils Jansen, and Ufuk Topcu. "Task-Aware Verifiable RNN-Based Policies for Partially Observable Markov Decision Processes." Journal of Artificial Intelligence Research 72 (November 18, 2021): 819–47. http://dx.doi.org/10.1613/jair.1.12963.

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Partially observable Markov decision processes (POMDPs) are models for sequential decision-making under uncertainty and incomplete information. Machine learning methods typically train recurrent neural networks (RNN) as effective representations of POMDP policies that can efficiently process sequential data. However, it is hard to verify whether the POMDP driven by such RNN-based policies satisfies safety constraints, for instance, given by temporal logic specifications. We propose a novel method that combines techniques from machine learning with the field of formal methods: training an RNN-b
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Kim, Sung-Kyun, Oren Salzman, and Maxim Likhachev. "POMHDP: Search-Based Belief Space Planning Using Multiple Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 734–44. http://dx.doi.org/10.1609/icaps.v29i1.3542.

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Robots operating in the real world encounter substantial uncertainty that cannot be modeled deterministically before the actual execution. This gives rise to the necessity of robust motion planning under uncertainty also known as belief space planning. Belief space planning can be formulated as Partially Observable Markov Decision Processes (POMDPs). However, computing optimal policies for non-trivial POMDPs is computationally intractable. Building upon recent progress from the search community, we propose a novel anytime POMDP solver, Partially Observable Multi-Heuristic Dynamic Programming (
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Wang, Chenggang, and Roni Khardon. "Relational Partially Observable MDPs." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1153–58. http://dx.doi.org/10.1609/aaai.v24i1.7742.

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Relational Markov Decision Processes (MDP) are a useful abstraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size or instantiation. While there has been an increased interest in developing relational fully observable MDPs, there has been very little work on relational partially observable MDPs (POMDP), which deal with uncertainty in problem states in addition to stochastic action effects. This paper provides a concrete formalization of relational POMDPs making several technical contributions toward their solution. First,
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Hauskrecht, M. "Value-Function Approximations for Partially Observable Markov Decision Processes." Journal of Artificial Intelligence Research 13 (August 1, 2000): 33–94. http://dx.doi.org/10.1613/jair.678.

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Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accurac
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Victorio-Meza, Hermilo, Manuel Mejía-Lavalle, Alicia Martínez Rebollar, Andrés Blanco Ortega, Obdulia Pichardo Lagunas, and Grigori Sidorov. "Searching for Cerebrovascular Disease Optimal Treatment Recommendations Applying Partially Observable Markov Decision Processes." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 01 (2017): 1860015. http://dx.doi.org/10.1142/s0218001418600157.

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Partially observable Markov decision processes (POMDPs) are mathematical models for the planning of action sequences under conditions of uncertainty. Uncertainty in POMDPs is manifested in two ways: uncertainty in the perception of model states and uncertainty in the effects of actions on states. The diagnosis and treatment of cerebral vascular diseases (CVD) present this double condition of uncertainty, so we think that POMDP is the most suitable method to model them. In this paper, we propose a model of CVD that is based on observations obtained from neuroimaging studies such as computed tom
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Zhang, N. L., and W. Liu. "A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains." Journal of Artificial Intelligence Research 7 (November 1, 1997): 199–230. http://dx.doi.org/10.1613/jair.419.

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Partially observable Markov decision processes (POMDPs) are a natural model for planning problems where effects of actions are nondeterministic and the state of the world is not completely observable. It is difficult to solve POMDPs exactly. This paper proposes a new approximation scheme. The basic idea is to transform a POMDP into another one where additional information is provided by an oracle. The oracle informs the planning agent that the current state of the world is in a certain region. The transformed POMDP is consequently said to be region observable. It is easier to solve than the or
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Maxtulus Junedy Nababan. "Perkembangan Perilaku Pembelajaran Peserta Didik dengan Menggunakan Partially Observable Markov Decision Processes." Edukasi Elita : Jurnal Inovasi Pendidikan 2, no. 1 (2024): 289–97. https://doi.org/10.62383/edukasi.v2i1.1034.

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The School Community can be considered as an agent in a multi-agent system because there is dynamic interaction in a school system. The important thing in a multi-agent system is managing relationships between agents to achieve coordinated behavior by developing knowledge, attitudes and practices, namely the factors that shape environmental behavior. This research extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the idea of ​​agent models into the state space. The results of this research show that (POMDP) ​​is effective
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Omidshafiei, Shayegan, Ali–Akbar Agha–Mohammadi, Christopher Amato, Shih–Yuan Liu, Jonathan P. How, and John Vian. "Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions." International Journal of Robotics Research 36, no. 2 (2017): 231–58. http://dx.doi.org/10.1177/0278364917692864.

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This work focuses on solving general multi-robot planning problems in continuous spaces with partial observability given a high-level domain description. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This work extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) to take advantage of the high-level representations that are natural for multi-robot problems and to facil
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Dissertations / Theses on the topic "Partially Observable Markov Decision Processes (POMDPs)"

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Aberdeen, Douglas Alexander, and doug aberdeen@anu edu au. "Policy-Gradient Algorithms for Partially Observable Markov Decision Processes." The Australian National University. Research School of Information Sciences and Engineering, 2003. http://thesis.anu.edu.au./public/adt-ANU20030410.111006.

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Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms are computationally intractable. Such computational complexity motivates approximate approaches. One such class of algorithms are the so-called policy-gradient methods from reinforcement learning. They seek to adjust the parameters of an agent in the direction that maximises the long-term average
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Olafsson, Björgvin. "Partially Observable Markov Decision Processes for Faster Object Recognition." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632.

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Object recognition in the real world is a big challenge in the field of computer vision. Given the potentially enormous size of the search space it is essential to be able to make intelligent decisions about where in the visual field to obtain information from to reduce the computational resources needed. In this report a POMDP (Partially Observable Markov Decision Process) learning framework, using a policy gradient method and information rewards as a training signal, has been implemented and used to train fixation policies that aim to maximize the information gathered in each fixation. The p
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Lusena, Christopher. "Finite Memory Policies for Partially Observable Markov Decision Proesses." UKnowledge, 2001. http://uknowledge.uky.edu/gradschool_diss/323.

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This dissertation makes contributions to areas of research on planning with POMDPs: complexity theoretic results and heuristic techniques. The most important contributions are probably the complexity of approximating the optimal history-dependent finite-horizon policy for a POMDP, and the idea of heuristic search over the space of FFTs.
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Torre, tresols Juan Jesús. "The partially observable brain : An exploratory study on the use of partially observable Markov decision processes as a general framework for brain-computer interfaces." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0038.

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Malgré les progrès récents, les interfaces cerveau-machine (ICM) continuent de se heurter à plusieurs limites qui entravent leur passage des laboratoires aux applications réelles. Les problèmes persistants des faux positifs et des temps de décodage fixes sont particulièrement préoccupants. Cette thèse propose l'intégration d'un cadre décisionnel structuré, en particulier le processus de décision markovien partiellement observable (POMDP), dans la technologie BCI.Notre objectif est de concevoir un modèle POMDP complet adaptable à diverses modalités d'ICB, servant de composante décisionnelle dan
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Skoglund, Caroline. "Risk-aware Autonomous Driving Using POMDPs and Responsibility-Sensitive Safety." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300909.

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Autonomous vehicles promise to play an important role aiming at increased efficiency and safety in road transportation. Although we have seen several examples of autonomous vehicles out on the road over the past years, how to ensure the safety of autonomous vehicle in the uncertain and dynamic environment is still a challenging problem. This thesis studies this problem by developing a risk-aware decision making framework. The system that integrates the dynamics of an autonomous vehicle and the uncertain environment is modelled as a Partially Observable Markov Decision Process (POMDP). A risk m
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You, Yang. "Probabilistic Decision-Making Models for Multi-Agent Systems and Human-Robot Collaboration." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0014.

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Dans cette thèse, nous nous intéressons à la prise de décision haut niveau (planification de tâches) pour la robotique à l'aide de modèles de prise de décision markoviens et sous deux aspects : la collaboration robot-robot et la collaboration homme-robot. Dans le cadre de la collaboration robot-robot (RRC), nous étudions les problèmes de décision de plusieurs robots devant atteindre un objectif commun de manière collaborative, et nous utilisons le cadre des processus de décision markoviens partiellement observables et décentralisés (Dec-POMDP) pour modéliser de tels problèmes. Nous proposons d
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Cheng, Hsien-Te. "Algorithms for partially observable Markov decision processes." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/29073.

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The thesis develops methods to solve discrete-time finite-state partially observable Markov decision processes. For the infinite horizon problem, only discounted reward case is considered. Several new algorithms for the finite horizon and the infinite horizon problems are developed. For the finite horizon problem, two new algorithms are developed. The first algorithm is called the relaxed region algorithm. For each support in the value function, this algorithm determines a region not smaller than its support region and modifies it implicitly in later steps until the exact support region is fo
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Jaulmes, Robin. "Active learning in partially observable Markov decision processes." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98733.

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People are efficient when they make decisions under uncertainty, even when their decisions have long-term ramifications, or when their knowledge and their perception of the environment are uncertain. We are able to experiment with the environment and learn, improving our behavior as experience is gathered. Most of the problems we face in real life are of that kind, and most of the problems that an automated agent would face in robotics too.<br>Our goal is to build Artificial Intelligence algorithms able to reproduce the reasoning of humans for these complex problems. We use the Reinforcement L
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Aberdeen, Douglas Alexander. "Policy-gradient algorithms for partially observable Markov decision processes /." View thesis entry in Australian Digital Theses Program, 2003. http://thesis.anu.edu.au/public/adt-ANU20030410.111006/index.html.

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Zawaideh, Zaid. "Eliciting preferences sequentially using partially observable Markov decision processes." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18794.

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Decision Support systems have been gaining in importance recently. Yet one of the bottlenecks of designing such systems lies in understanding how the user values different decision outcomes, or more simply what the user preferences are. Preference elicitation promises to remove the guess work of designing decision making agents by providing more formal methods for measuring the `goodness' of outcomes. This thesis aims to address some of the challenges of preference elicitation such as the high dimensionality of the underlying problem. The problem is formulated as a partially observable Markov
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Books on the topic "Partially Observable Markov Decision Processes (POMDPs)"

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Howes, Andrew, Xiuli Chen, Aditya Acharya, and Richard L. Lewis. Interaction as an Emergent Property of a Partially Observable Markov Decision Process. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0011.

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In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scena
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Poupart, Pascal. Exploiting structure to efficiently solve large scale partially observable Markov decision processes. 2005.

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McAbee, Ashley. Moving Target Defense Scheme with Overhead Optimization Using Partially Observable Markov Decision Processes with Absorbing States. Independently Published, 2021.

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Book chapters on the topic "Partially Observable Markov Decision Processes (POMDPs)"

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Andriushchenko, Roman, Alexander Bork, Milan Češka, Sebastian Junges, Joost-Pieter Katoen, and Filip Macák. "Search and Explore: Symbiotic Policy Synthesis in POMDPs." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37709-9_6.

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AbstractThis paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller—that solely decides based on the observations seen so far—to achieve a total expected reward objective. As finding optimal controllers is undecidable, we concentrate on synthesising good finite-state controllers (FSCs). We do so by tightly integrating two modern, orthogonal methods for POMDP controller synthesis: a belief-based and an inductive ap
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Bork, Alexander, Joost-Pieter Katoen, and Tim Quatmann. "Under-Approximating Expected Total Rewards in POMDPs." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_2.

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AbstractWe consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold? We tackle this—generally undecidable—problem by computing under-approximations on these total expected rewards. This is done by abstracting finite unfoldings of the infinite belief MDP of the POMDP. The key issue is to find a suitable under-approximation of the value function. We provide two techniques: a simple (cut-off) technique that uses a good policy on the POMDP, and a more advanced technique (belief clipping) that
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Bork, Alexander, Debraj Chakraborty, Kush Grover, Jan Křetínský, and Stefanie Mohr. "Learning Explainable and Better Performing Representations of POMDP Strategies." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57249-4_15.

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AbstractStrategies for partially observable Markov decision processes (POMDP) typically require memory. One way to represent this memory is via automata. We present a method to learn an automaton representation of a strategy using a modification of the $$L^*$$ L ∗ -algorithm. Compared to the tabular representation of a strategy, the resulting automaton is dramatically smaller and thus also more explainable. Moreover, in the learning process, our heuristics may even improve the strategy’s performance. We compare our approach to an existing approach that synthesizes an automaton directly from th
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Bäuerle, Nicole, and Ulrich Rieder. "Partially Observable Markov Decision Processes." In Universitext. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18324-9_5.

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Dutech, Alain, and Bruno Scherrer. "Partially Observable Markov Decision Processes." In Markov Decision Processes in Artificial Intelligence. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557426.ch7.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Partially Observable Markov Decision Processes." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_629.

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Sucar, Luis Enrique. "Partially Observable Markov Decision Processes." In Probabilistic Graphical Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_12.

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Spaan, Matthijs T. J. "Partially Observable Markov Decision Processes." In Adaptation, Learning, and Optimization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_12.

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Poupart, Pascal. "Partially Observable Markov Decision Processes." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_629.

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Besse, Camille, and Brahim Chaib-draa. "Quasi-Deterministic Partially Observable Markov Decision Processes." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_27.

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Conference papers on the topic "Partially Observable Markov Decision Processes (POMDPs)"

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Pitarch, Jos� L., Leopoldo Armesto, and Antonio Sala. "Nonmyopic Bayesian process optimization with a finite budget." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.155555.

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Optimization under uncertainty is inherent to many PSE applications ranging from process design to RTO. Reaching process true optima often involves learning from experimentation, but actual experiments involve a cost (economic, resources, time) and a budget limit usually exists. Finding the best trade-off on cumulative process performance and experimental cost over a finite budget is a Partially Observable Markov Decision Process (POMDP), known to be computationally intractable. This paper follows the nonmyopic Bayesian optimization (BO) approximation to POMDPs developed by the machine-learnin
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Williams, Jason D., Pascal Poupart, and Steve Young. "Partially Observable Markov Decision Processes with Continuous Observations for Dialogue Management." In Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue. Special Interest Group on Discourse and Dialogue (SIGdial), 2005. https://doi.org/10.18653/v1/2005.sigdial-1.4.

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Soh, Harold, and Yiannis Demiris. "Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)." In the 13th annual conference. ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001674.

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Castellini, Alberto, Georgios Chalkiadakis, and Alessandro Farinelli. "Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/769.

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Online planning methods for partially observable Markov decision processes (POMDPs) have recently gained much interest. In this paper, we propose the introduction of prior knowledge in the form of (probabilistic) relationships among discrete state-variables, for online planning based on the well-known POMCP algorithm. In particular, we propose the use of hard constraint networks and probabilistic Markov random fields to formalize state-variable constraints and we extend the POMCP algorithm to take advantage of these constraints. Results on a case study based on Rocksample show that the usage o
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Carr, Steven, Nils Jansen, Ralf Wimmer, Alexandru Serban, Bernd Becker, and Ufuk Topcu. "Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/768.

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We study strategy synthesis for partially observable Markov decision processes (POMDPs). The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints. This problem is computationally intractable and theoretically hard. We propose a novel method that combines techniques from machine learning and formal verification. First, we train a recurrent neural network (RNN) to encode POMDP strategies. The RNN accounts for memory-based decisions without the need to expand the full belief space of a POMDP. Secondly, we restrict the RNN-based strategy
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Lim, Michael H., Claire Tomlin, and Zachary N. Sunberg. "Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/572.

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Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown unprecedented effectiveness in domains with continuous observation spaces. However there has been no formal theoretical justification for this technique. This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS
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Lim, Michael, Tyler Becker, Mykel Kochenderfer, Claire Tomlin, and Zachary Sunberg. "Optimality Guarantees for Particle Belief Approximation of POMDPs (Abstract Reprint)." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/953.

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Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Ou
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Bovy, Eline M., Marnix Suilen, Sebastian Junges, and Nils Jansen. "Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/740.

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Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) sem
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Gonçalves, Luciano V., Graçaliz P. Dimuro, and Antônio Carlos da R. Costa. "Uma arquitetura de Agentes BDI para auto-regulação de Trocas Sociais em Sistemas Multiagentes Abertos." In Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações. Sociedade Brasileira de Computação, 2009. https://doi.org/10.5753/wesaac.2009.33097.

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Este trabalho apresenta uma arquitetura de agentes híbrida para a auto-regulação de trocas sociais executadas entre pares de agentes baseados em traços de personalidade que operam em sistemas multiagentes abertos. A arquitetura proposta segue os conceitos da arquitetura BDI (Beliefs, Desires, Intentions), adicionando um módulo de descoberta de traços de personalidade, através dos HMMs (Hidden Markov Models) e um módulo de especificação de novos planos para o controle dos agentes, através de políticas ótimas de POMDPs (Partially Observable Markov Decision Processes). O trabalho conjunto de HMMs
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Hsiao, Chuck, and Richard Malak. "Modeling Information Gathering Decisions in Systems Engineering Projects." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34854.

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Decisions in systems engineering projects commonly are made under significant amounts of uncertainty. This uncertainty can exist in many areas such as the performance of subsystems, interactions between subsystems, or project resource requirements such as budget or personnel. System engineers often can choose to gather information that reduces uncertainty, which allows for potentially better decisions, but at the cost of resources expended in acquiring the information. However, our understanding of how to analyze situations involving gathering information is limited, and thus heuristics, intui
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You might also be interested in the extended bibliographies on the topic 'Partially Observable Markov Decision Processes (POMDPs)' for particular source types:
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