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Статті в журналах з теми "Partially Observable Markov Decision Processes (POMDPs)":
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 (December 2013): 821–63. http://dx.doi.org/10.1142/s0218488513500396.
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 (April 3, 2020): 10276–83. http://dx.doi.org/10.1609/aaai.v34i06.6590.
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.
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.
Wang, Chenggang, and Roni Khardon. "Relational Partially Observable MDPs." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1153–58. http://dx.doi.org/10.1609/aaai.v24i1.7742.
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.
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 (October 9, 2017): 1860015. http://dx.doi.org/10.1142/s0218001418600157.
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.
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 (February 2017): 231–58. http://dx.doi.org/10.1177/0278364917692864.
Rozek, Brandon, Junkyu Lee, Harsha Kokel, Michael Katz, and Shirin Sohrabi. "Partially Observable Hierarchical Reinforcement Learning with AI Planning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23635–36. http://dx.doi.org/10.1609/aaai.v38i21.30504.
Дисертації з теми "Partially Observable Markov Decision Processes (POMDPs)":
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.
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.
Lusena, Christopher. "Finite Memory Policies for Partially Observable Markov Decision Proesses." UKnowledge, 2001. http://uknowledge.uky.edu/gradschool_diss/323.
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.
Autonoma fordon förutspås spela en stor roll i framtiden med målen att förbättra effektivitet och säkerhet för vägtransporter. Men även om vi sett flera exempel av autonoma fordon ute på vägarna de senaste åren är frågan om hur säkerhet ska kunna garanteras ett utmanande problem. Det här examensarbetet har studerat denna fråga genom att utveckla ett ramverk för riskmedvetet beslutsfattande. Det autonoma fordonets dynamik och den oförutsägbara omgivningen modelleras med en partiellt observerbar Markov-beslutsprocess (POMDP från engelskans “Partially Observable Markov Decision Process”). Ett riskmått föreslås baserat på ett säkerhetsavstånd förkortat RSS (från engelskans “Responsibility-Sensitive Safety”) som kvantifierar det minsta avståndet till andra fordon för garanterad säkerhet. Riskmåttet integreras i POMDP-modellens belöningsfunktion för att åstadkomma riskmedvetna beteenden. Den föreslagna riskmedvetna POMDP-modellen utvärderas i två fallstudier. I ett scenario där det egna fordonet följer ett annat fordon på en enfilig väg visar vi att det egna fordonet kan undvika en kollision då det framförvarande fordonet bromsar till stillastående. I ett scenario där det egna fordonet ansluter till en huvudled från en ramp visar vi att detta görs med ett tillfredställande avstånd till andra fordon. Slutsatsen är att den riskmedvetna POMDP-modellen lyckas realisera en avvägning mellan säkerhet och användbarhet genom att hålla ett rimligt säkerhetsavstånd och anpassa sig till andra fordons beteenden.
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.
In this thesis, using Markov decision models, we investigate high-level decision-making (task-level planning) for robotics in two aspects: robot-robot collaboration and human-robot collaboration.In robot-robot collaboration (RRC), we study the decision problems of multiple robots involved to achieve a shared goal collaboratively, and we use the decentralized partially observable Markov decision process (Dec-POMDP) framework to model such RRC problems. Then, we propose two novel algorithms for solving Dec-POMDPs. The first algorithm (Inf-JESP) finds Nash equilibrium solutions by iteratively building the best-response policy for each agent until no improvement can be made. To handle infinite-horizon Dec-POMDPs, we represent each agent's policy using a finite-state controller. The second algorithm (MC-JESP) extends Inf-JESP with generative models, which enables us to scale up to large problems. Through experiments, we demonstrate our methods are competitive with existing Dec-POMDP solvers.In human-robot collaboration (HRC), we can only control the robot, and the robot faces uncertain human objectives and induced behaviors. Therefore, we attempt to address the challenge of deriving robot policies in HRC, which are robust to the uncertainties about human behaviors. In this direction, we discuss possible mental models that can be used to model humans in an HRC task. We propose a general approach to derive, automatically and without prior knowledge, a model of human behaviors based on the assumption that the human could also control the robot. From here, we then design two algorithms for computing robust robot policies relying on solving a robot POMDP, whose state contains the human's internal state. The first algorithm operates offline and gives a complete robot policy that can be used during the robot's execution. The second algorithm is an online method, i.e., it plans the robot's action at each time step during execution. Compared with the offline approach, the online method only requires a generative model and thus can scale up to large problems. Experiments with synthetic and real humans are conducted in a simulated environment to evaluate these algorithms. We observe that our methods can provide robust robot decisions despite the uncertainties over human objectives and behaviors.In this thesis, our research for RRC provides a foundation for building best-response policies in a partially observable and multi-agent setting, which serves as an important intermediate step for addressing HRC problems. Moreover, we provide more flexible algorithms using generative models in each contribution, and we believe this will facilitate applying our contributions to real-world applications
Cheng, Hsien-Te. "Algorithms for partially observable Markov decision processes." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/29073.
Business, Sauder School of
Graduate
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.
Our goal is to build Artificial Intelligence algorithms able to reproduce the reasoning of humans for these complex problems. We use the Reinforcement Learning framework, which allows to learn optimal behaviors in dynamic environments. More precisely, we adapt Partially-Observable Markov Decision Processes (POMDPs) to environments that are partially known.
We take inspiration from the field of Active Learning: we assume the existence of an oracle, who can, during a short learning phase, provide the agent with additional information about its environment. The agent actively learns everything that is useful in the environment, with a minimum use of the oracle.
After reviewing existing methods for solving learning problems in partially observable environments, we expose a theoretical active learning setup. We propose an algorithm, MEDUSA, and show theoretical and empirical proofs of performance for it.
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.
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.
Les systèmes d'aide à la décision ont gagné en importance récemment. Pourtant, un des problèmes importants liés au design de tels systèmes demeure: comprendre comment l'usager évalue les différents résultats, ou plus simplement, déterminer quelles sont ses préférences. L'extraction des préférences vise à éliminer certains aspects arbitraires du design d'agents de décision en offrant des méthodes plus formelles pour mesurer la qualité des résultats. Cette thèse tente de résoudre certains problèmes ayant trait à l'extraction des préférences, tel que celui de la haute dimensionnalité du problème sous-jacent. Le problème est formulé en tant que processus de décision markovien partiellement observable (POMDP), et utilise une représentation factorisée afin de profiter de la structure inhérente aux problèmes d'extraction des préférences. De plus, des connaissances simples quant aux caractéristiques de ces problèmes sont exploitées afin d'obtenir des préférences plus précises, sans pour autant augmenter la tâche de l'usager. Les actions terminales "sparse" sont définies de manière à permettre un compromis flexible entre vitesse et précision. Le résultat est un système assez flexible pour être appliqué à un grand nombre de domaines qui ont à faire face aux problèmes liés aux méthodes d'extraction des préférences.
Williams, Jason Douglas. "Partially observable Markov decision processes for spoken dialogue management." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612754.
Книги з теми "Partially Observable Markov Decision Processes (POMDPs)":
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.
Poupart, Pascal. Exploiting structure to efficiently solve large scale partially observable Markov decision processes. 2005.
Частини книг з теми "Partially Observable Markov Decision Processes (POMDPs)":
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, 113–35. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37709-9_6.
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, 22–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_2.
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, 299–319. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57249-4_15.
Bäuerle, Nicole, and Ulrich Rieder. "Partially Observable Markov Decision Processes." In Universitext, 147–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18324-9_5.
Dutech, Alain, and Bruno Scherrer. "Partially Observable Markov Decision Processes." In Markov Decision Processes in Artificial Intelligence, 185–228. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557426.ch7.
Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Partially Observable Markov Decision Processes." In Encyclopedia of Machine Learning, 754–60. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_629.
Sucar, Luis Enrique. "Partially Observable Markov Decision Processes." In Probabilistic Graphical Models, 249–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_12.
Spaan, Matthijs T. J. "Partially Observable Markov Decision Processes." In Adaptation, Learning, and Optimization, 387–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_12.
Poupart, Pascal. "Partially Observable Markov Decision Processes." In Encyclopedia of Machine Learning and Data Mining, 959–66. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_629.
Besse, Camille, and Brahim Chaib-draa. "Quasi-Deterministic Partially Observable Markov Decision Processes." In Neural Information Processing, 237–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_27.
Тези доповідей конференцій з теми "Partially Observable Markov Decision Processes (POMDPs)":
Soh, Harold, and Yiannis Demiris. "Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001674.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/769.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/768.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/572.
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.
Horák, Karel, Branislav Bošanský, and Krishnendu Chatterjee. "Goal-HSVI: Heuristic Search Value Iteration for Goal POMDPs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/662.
Koops, Wietze, Nils Jansen, Sebastian Junges, and Thiago D. Simão. "Recursive Small-Step Multi-Agent A* for Dec-POMDPs." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/600.
Carr, Steven, Nils Jansen, and Ufuk Topcu. "Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/570.
Chatterjee, Krishnendu, Adrián Elgyütt, Petr Novotný, and Owen Rouillé. "Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-Sum Objectives." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/652.
Dam, Tuan, Pascal Klink, Carlo D'Eramo, Jan Peters, and Joni Pajarinen. "Generalized Mean Estimation in Monte-Carlo Tree Search." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/332.