Specifically, in one of the works below we focus on belief simplification, in terms of using only a subset of state samples, and use it to formulate bounds on the corresponding original belief-dependent rewards. These bounds in turn are used to perform branch pruning over the belief tree, in the process of calculating the optimal policy. We further introduce the notion of adaptive simplification, while re-using calculations between different simplification levels, and exploit it to prune, at each level in the belief tree, all branches but one. Therefore, our approach is guaranteed to find the optimal solution of the original problem but with substantial speedup. As a second key contribution, we derive novel analytical bounds for differential entropy, considering a sampling-based belief representation, which we believe are of interest on their own.
In another work we introduced a framework for quantifying online the effect of a simplification, alongside novel stochastic bounds on the return. Our bounds take advantage of the information encoded in the joint distribution of the original and simplified return. The proposed general framework is applicable to any bounds on the return to capture simplification outcomes.
Related Publications:
Journal Articles
- A. Zhitnikov and V. Indelman, “Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints,” IEEE Transactions on Robotics (T-RO), 2024.
- A. Zhitnikov, O. Sztyglic, and V. Indelman, “No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification,” International Journal of Robotics Research (IJRR), accepted, 2024.
- T. Yotam and V. Indelman, “Measurement Simplification in ρ-POMDP with Performance Guarantees,” IEEE Transactions on Robotics (T-RO), 2024.
- M. Barenboim, M. Shienman, and V. Indelman, “Monte Carlo Planning in Hybrid Belief POMDPs,” IEEE Robotics and Automation Letters (RA-L), no. 8, Aug. 2023.
- M. Barenboim, I. Lev-Yehudi, and V. Indelman, “Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees,” IEEE Robotics and Automation Letters (RA-L), no. 10, Oct. 2023.
- A. Zhitnikov and V. Indelman, “Simplified Risk Aware Decision Making with Belief Dependent Rewards in Partially Observable Domains,” Artificial Intelligence, Special Issue on “Risk-Aware Autonomous Systems: Theory and Practice", Aug. 2022.
Conference Articles
- I. Lev-Yehudi, M. Barenboim, and V. Indelman, “Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice,” in 38th AAAI Conference on Artificial Intelligence (AAAI-24), Feb. 2024.
- A. Zhitnikov and V. Indelman, “Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains,” in International Joint Conference on Artificial Intelligence (IJCAI), journal track, Aug. 2023.
- M. Barenboim and V. Indelman, “Online POMDP Planning with Anytime Deterministic Guarantees,” in Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023.
- M. Shienman and V. Indelman, “D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints,” in IEEE International Conference on Robotics and Automation (ICRA), *Outstanding Paper Award Finalist*, May 2022.
- M. Barenboim and V. Indelman, “Adaptive Information Belief Space Planning,” in the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI), Jul. 2022.
- O. Sztyglic and V. Indelman, “Speeding up POMDP Planning via Simplification,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2022.
- M. Shienman and V. Indelman, “Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints,” in International Symposium on Robotics Research (ISRR), Sep. 2022.
Technical Reports
- Y. Pariente and V. Indelman, “Simplification of Risk Averse POMDPs with Performance Guarantees,” 2024.
- G. Rotman and V. Indelman, “involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs,” Sep. 2022.
- A. Zhitnikov and V. Indelman, “Probabilistic Loss and its Online Characterization
for Simplified Decision Making Under Uncertainty,” 2021.
- O. Sztyglic, A. Zhitnikov, and V. Indelman, “Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency,” May 2021.
Book Chapters
- M. Shienman and V. Indelman, “Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints,” in Robotics Research, Springer, 2023.
Theses
- T. Yotam, “Measurement Simplification in ρ-POMDP with Performance Guarantees,” Master's thesis, Technion - Israel Institute of Technology, 2023.
- G. Rotman, “Efficient Informative Planning with High-dimensional Non-Gaussian Beliefs by Exploiting Structure,” Master's thesis, Technion - Israel Institute of Technology, 2022.
- O. Sztyglic, “Online Partially Observable Markov Decision Process Planning via Simplification,” Master's thesis, Technion - Israel Institute of Technology, 2021.