# Collaborative Agent Gameplay in the Pandemic Board Game

arXiv: https://arxiv.org/abs/2103.11388 · HTML: https://mlsanity.com/paper/2103.11388/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 21 Mar 2021.

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

- [The Many AI Challenges of Hearthstone](https://mlsanity.com/paper/1907.06562/) (2019)
- [Evaluating and Modelling Hanabi-Playing Agents](https://mlsanity.com/paper/1704.07069/) (2017)
- [Biasing MCTS with Features for General Games](https://mlsanity.com/paper/1903.08942/) (2019)
- [Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor](https://mlsanity.com/paper/1904.01883/) (2019)
- [A Local Approach to Forward Model Learning: Results on the Game of Life Game](https://mlsanity.com/paper/1903.12508/) (2019)

## Built on by (most cited first, 3 of 3)

- [Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic](https://mlsanity.com/paper/2103.15090/) (2021)
- [Enhancing team performance with transfer-learning during real-world human-robot collaboration](https://mlsanity.com/paper/2211.13070/) (2022)
- [Seeding for Success: Skill and Stochasticity in Tabletop Games](https://mlsanity.com/paper/2503.02686/) (2025)

## Abstract (arXiv)

While artificial intelligence has been applied to control players' decisions in board games for over half a century, little attention is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to overcome challenges posed by events occurring during the game's progression. This paper proposes an artificial agent which controls all players' actions and balances chances of winning versus risk of losing in this highly stochastic environment. The agent applies a Rolling Horizon Evolutionary Algorithm on an abstraction of the game-state that lowers the branching factor and simulates the game's stochasticity. Results show that the proposed algorithm can find winning strategies more consistently in different games of varying difficulty. The impact of a number of state evaluation metrics is explored, balancing between optimistic strategies that favor winning and pessimistic strategies that guard against losing.