6 citations · 11 across the 9 of their papers we have counts for
9 papers
Effective Game-Theoretic Motion Planning via Nested Search
Avishav Engle, Andrey Zhitnikov, Oren Salzman +2
To facilitate effective, safe deployment in the real world, individual robots must reason about interactions with other agents, which often occur without explicit communication. Re…
Anytime Incremental POMDP Planning in Continuous Spaces
Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov +1
Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic expl…
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
Andrey Zhitnikov, Vadim Indelman
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon th…
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…
Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints
Andrey Zhitnikov, Vadim Indelman
Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in robotics and Artificial Intelligence. Due…
Risk Aware Adaptive Belief-dependent Probabilistically Constrained Continuous POMDP Planning
Andrey Zhitnikov, Vadim Indelman
Although risk awareness is fundamental to an online operating agent, it has received less attention in the challenging continuous domain and under partial observability. This paper…
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…
Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty
Andrey Zhitnikov, Vadim Indelman
It is a long-standing objective to ease the computation burden incurred by the decision making process. Identification of this mechanism's sensitivity to simplification has tremend…
Topology of deep neural networks
Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim
We study how the topology of a data set , representing two classes and in a binary classification problem, changes as it passes thr…