---
title: "Mixing Search Strategies for Multi-Player Games"
authors:
  - "Inon Zuckerman"
  - "Ariel Felner"
  - "Sarit Kraus"
doi: ""
url: "https://ijcai.org/proceedings/2009/113"
pdf: "https://ijcai.org/proceedings/2009/0113.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "646-651"
track: ""
keywords:
---

# Mixing Search Strategies for Multi-Player Games

Inon Zuckerman, Ariel Felner, Sarit Kraus

*Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence*, pages 646-651.

## Abstract

There are two basic approaches to generalize the propagation mechanism of the two-player Minimax search algorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main shortcoming of these approaches is that their strategy is fixed. In this paper we suggest a new approach (called MP-Mix) that dynamically changes the propagation strategy based on the players' relative strengths between MaxN, Paranoid and a newly presented offensive strategy. In addition, we introduce the Opponent Impact factor for multi-player games, which measures the players' ability to impact their opponents' score, and show its relation to the relative performance of our new MP-Mix strategy. Experimental results show that MP-Mix outperforms all other approaches under most circumstances.Inon Zuckerman, Ariel Felner, Sarit Kraus