# Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning

arXiv: https://arxiv.org/abs/2111.01825 · HTML: https://mlsanity.com/paper/2111.01825/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2021.

## Built on by (most cited first, 7 of 7)

- [C-MCTS: Safe Planning with Monte Carlo Tree Search](https://mlsanity.com/paper/2305.16209/) (2023)
- [A Search and Detection Autonomous Drone System: from Design to Implementation](https://mlsanity.com/paper/2211.15866/) (2022)
- [CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation](https://mlsanity.com/paper/2604.23307/) (2026)
- [Cost Aware Asynchronous Multi-Agent Active Search](https://mlsanity.com/paper/2210.02259/) (2022)
- [Pareto-NRPA: A Novel Monte-Carlo Search Algorithm for Multi-Objective Optimization](https://mlsanity.com/paper/2507.19109/) (2025)
- [Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models](https://mlsanity.com/paper/2508.15220/) (2025)
- [MoFlow: Multi-Objective Agentic Workflow Generation](https://mlsanity.com/paper/2609.38294/) (2026)

## Abstract (arXiv)

In many environmental monitoring scenarios, the sampling robot needs to simultaneously explore the environment and exploit features of interest with limited time. We present an anytime multi-objective informative planning method called Pareto Monte Carlo tree search which allows the robot to handle potentially competing objectives such as exploration versus exploitation. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment state, leading to better adaptation to environmental dynamics. We provide algorithmic analysis on the critical tree node selection step and show that the number of times choosing sub-optimal nodes is logarithmically bounded and the search result converges to the optimal choices at a polynomial rate.