# Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

arXiv: https://arxiv.org/abs/2103.15090 · HTML: https://mlsanity.com/paper/2103.15090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Pommerman: A Multi-Agent Playground](https://mlsanity.com/paper/1809.07124/) (2018)
- [Diverse Agents for Ad-Hoc Cooperation in Hanabi](https://mlsanity.com/paper/1907.03840/) (2019)
- [Evaluating and Modelling Hanabi-Playing Agents](https://mlsanity.com/paper/1704.07069/) (2017)
- [Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing](https://mlsanity.com/paper/1704.06942/) (2017)
- [Collaborative Agent Gameplay in the Pandemic Board Game](https://mlsanity.com/paper/2103.11388/) (2021)

## Built on by (most cited first, 1 of 1)

- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)

## Abstract (arXiv)

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.