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Anil Yildiz

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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JAIR Journal 2026 Journal Article

Backward Monte Carlo Tree Search: Charting Unsafe Regions in the Belief-Space

Anil Yildiz
Esen Yel
Marcell Vazquez-Chanlatte
Kyle Wray
Mykel J. Kochenderfer
Stefan J. Witwicki

Safety-critical systems often operate in partially observable environments, where assessing the safety of the underlying policy remains a fundamental challenge. This study focuses on evaluating policies by identifying regions of the belief-space that can lead the system’s policy to an undesirable state with a non-negligible probability. In this paper, we introduce Backward Monte Carlo Tree Search, the first Monte Carlo tree search framework that expands backward in time within the belief-space. The tree search begins from an undesired terminal belief and recursively explores its possible predecessors, constructing a tree of belief transitions that could lead to an unsafe outcome within a given horizon. Evaluations in gridworld and autonomous driving domains show that identifying beliefs from which failures may occur enables runtime risk forecasting and targeted policy retraining, marking a conceptual shift in how safety is validated under uncertainty.

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AAAI Conference 2021 Conference Paper

Bayesian Optimized Monte Carlo Planning

John Mern
Anil Yildiz
Zachary Sunberg
Tapan Mukerji
Mykel J. Kochenderfer

Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. The performance of progressive widening search is dependent upon the action sampling policy, often requiring problem-specific samplers. In this work, we present a general method for efficient action sampling based on Bayesian optimization. The proposed method uses a Gaussian process to model a belief over the action-value function and selects the action that will maximize the expected improvement in the optimal action value. We implement the proposed approach in a new online tree search algorithm called Bayesian Optimized Monte Carlo Planning (BOMCP). Several experiments show that BOMCP is better able to scale to large action space POMDPs than existing state-of-the-art tree search solvers.

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AAAI Conference 2021 Conference Paper

Improved POMDP Tree Search Planning with Prioritized Action Branching

John Mern
Anil Yildiz
Lawrence Bush
Tapan Mukerji
Mykel J. Kochenderfer

Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sample a subset of the action space for inclusion in a search tree. The proposed method first evaluates the action space according to a score function that is a linear combination of expected reward and expected information gain. The actions with the highest score are then added to the search tree during tree expansion. Using this score function, actions providing the highest expected mixture of exploration and exploitation are included in the tree. Experiments show that PA-POMCPOW is able to outperform existing state-of-theart solvers on problems with large discrete action spaces.

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