# Real-time tree search with pessimistic scenarios

arXiv: https://arxiv.org/abs/1902.10870 · HTML: https://mlsanity.com/paper/1902.10870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Developing a Successful Bomberman Agent](https://mlsanity.com/paper/2203.09608/) (2022)
- [School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget](https://mlsanity.com/paper/2102.11762/) (2021)
- [A Fast Evolutionary adaptation for MCTS in Pommerman](https://mlsanity.com/paper/2111.13770/) (2021)
- [Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman](https://mlsanity.com/paper/2305.13206/) (2023)

## Abstract (arXiv)

Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.