Evolving Agents for the Hanabi 2018 CIG Competition
This work addresses the challenge of creating effective AI agents for a specific game competition, representing an incremental improvement over existing methods.
The paper tackled the problem of developing agents for the cooperative card game Hanabi by using a genetic algorithm to evolve rule-based strategies, achieving scores superior to previously published research in mirror and mixed evaluations.
Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents.