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Добірка наукової літератури з теми "SMT, planning, POMDP, POMCP"

Автор: Grafiati

Опубліковано: 11 березня 2023

Оновлено: 31 липня 2025

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Статті в журналах з теми "SMT, planning, POMDP, POMCP"

Meli, Daniele, Alberto Castellini, and Alessandro Farinelli. "Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach." Journal of Artificial Intelligence Research 79 (February 28, 2024): 725–76. http://dx.doi.org/10.1613/jair.1.15826.

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Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific

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Meli, Daniele, Alberto Castellini, and Alessandro Farinelli. "Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28743. https://doi.org/10.1609/aaai.v39i27.35134.

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Mazzi, Giulio, Alberto Castellini, and Alessandro Farinelli. "Rule-based Shielding for Partially Observable Monte-Carlo Planning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 243–51. http://dx.doi.org/10.1609/icaps.v31i1.15968.

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Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. The lack of an explicit representation however hinders policy interpretability and makes policy verification very complex. In this work, we propose two contributions. The first is a method for identifying unexpected actions selected by POMCP with respect to expert prior knowledge of the task. The second is a shielding appro

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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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Zhang, Zongzhang, Michael Littman, and Xiaoping Chen. "Covering Number as a Complexity Measure for POMDP Planning and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1853–59. http://dx.doi.org/10.1609/aaai.v26i1.8360.

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Finding a meaningful way of characterizing the difficulty of partially observable Markov decision processes (POMDPs) is a core theoretical problem in POMDP research. State-space size is often used as a proxy for POMDP difficulty, but it is a weak metric at best. Existing work has shown that the covering number for the reachable belief space, which is a set of belief points that are reachable from the initial belief point, has interesting links with the complexity of POMDP planning, theoretically. In this paper, we present empirical evidence that the covering number for the reachable belief spa

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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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Ye, Nan, Adhiraj Somani, David Hsu, and Wee Sun Lee. "DESPOT: Online POMDP Planning with Regularization." Journal of Artificial Intelligence Research 58 (January 26, 2017): 231–66. http://dx.doi.org/10.1613/jair.5328.

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The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. W

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Chatterjee, Krishnendu, Martin Chmelik, and Ufuk Topcu. "Sensor Synthesis for POMDPs with Reachability Objectives." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 47–55. http://dx.doi.org/10.1609/icaps.v28i1.13875.

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Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors. We study a setting in which the sensors are only partially defined and the goal is to synthesize “weakest” additional sensors, such that in the resulting POMDP, there is a small-memory policy for the agent that almost-surely (with probability 1) satisfies a reachability objective. We show that the problem is NP-complete, and present a symbolic algorithm by encoding the problem into SAT instances. We illustr

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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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Spaan, M. T. J., and N. Vlassis. "Perseus: Randomized Point-based Value Iteration for POMDPs." Journal of Artificial Intelligence Research 24 (August 1, 2005): 195–220. http://dx.doi.org/10.1613/jair.1659.

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Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Co

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Більше джерел

Дисертації з теми "SMT, planning, POMDP, POMCP"

Mazzi, Giulio, Alberto Castellini, and Alessandro Farinelli. "Rule-Based Policy Interpretation and Shielding for Partially Observable Monte Carlo Planning." Doctoral thesis, 2022. http://hdl.handle.net/11562/1067927.

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Анотація:

Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm that can generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. However, the lack of an explicit representation of the policy hinders interpretability. In this thesis, we propose a methodology based on Maximum Satisfiability Modulo Theory (MAX-SMT) for analyzing POMCP policies by inspecting their traces, namely, sequences of belief-action pairs generated by the algorithm. The proposed

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Тези доповідей конференцій з теми "SMT, planning, POMDP, POMCP"

Wang, Yunbo, Bo Liu, Jiajun Wu, et al. "DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs." 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.2020/579.

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A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal compon

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ARCIERI, GIACOMO, CYPRIEN HOELZL, OLIVER SCHWERY, DANIEL STRAUB, KONSTANTINOS G. PAPAKONSTANTINOU, and ELENI CHATZI. "A COMPARISON OF VALUE-BASED AND POLICY-BASED REINFORCEMENT LEARNING FOR MONITORINGINFORMED RAILWAY MAINTENANCE PLANNING." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37015.

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Optimal maintenance planning for railway infrastructure and assets forms a complex sequential decision-making problem. Railways are naturally subject to deterioration, which can result in compromised service and increased safety risks and costs. Maintenance actions ought to be proactively planned to prevent the adverse effects of deterioration and the associated costs. Such predictive actions can be planned based on monitoring data, which are often indirect and noisy, thus offering an uncertain assessment of the railway condition. From a mathematical perspective, this forms a stochastic contro

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Yang, Shuo, Xinjun Mao, and Wanwei Liu. "Towards an Extended POMDP Planning Approach with Adjoint Action Model for Robotic Task." In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9283277.

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Phan, Thomy, Thomas Gabor, Robert Müller, Christoph Roch, and Claudia Linnhoff-Popien. "Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop 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.2019/778.

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Анотація:

We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning. SYMBOL maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded by the planning horizon and can be automatically adapted according to the underlying domain without any prior domain knowledge beyond a generative model. We empirically test SYMBOL in four large POMDP benchmark problems to demonstrate its effectiveness and robustness w.r.t. the choice of hyperparameters and evaluate its adaptive memory consumption. We also compare it

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