2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2023★ 1 cited
No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification
Andrey Zhitnikov, Ori Sztyglic, Vadim Indelman
Continuous POMDPs with general belief-dependent rewards are notoriously difficult to solve online. In this paper, we present a complete provable theory of adaptive multilevel simpl…
cs.AI2021★ 2 cited
Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency
Ori Sztyglic, Andrey Zhitnikov, Vadim Indelman
Partially Observable Markov Decision Processes (POMDPs) are notoriously hard to solve. Most advanced state-of-the-art online solvers leverage ideas of Monte Carlo Tree Search (MCTS…
cs.AI2021★ 1 cited
Online POMDP Planning via Simplification
Ori Sztyglic, Vadim Indelman
In this paper, we consider online planning in partially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. O…