# Best-First Width Search for Multi Agent Privacy-preserving Planning

## TL;DR

This paper introduces a novel best-first width search method tailored for multi-agent privacy-preserving planning, demonstrating its effectiveness in benchmark domains despite privacy constraints and heuristic approximations.

## Contribution

It adapts best-first width search to decentralized multi-agent planning, addressing privacy challenges and improving search efficiency over existing methods.

## Key findings

- Effective in multiple benchmark domains
- Performs well with heuristics that do not use private information
- Outperforms state-of-the-art approaches in experiments

## Abstract

In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.