Adapting to Teammates in a Cooperative Language Game
Christopher Archibald Spencer Brosnahan
The game of Codenames has recently emerged as a domain of interest for
intelligent agent design. The game is unique due to the way that language and
coordination between teammates play important roles. Previous approaches to
designing agents for this game have utilized a single internal language model
to determine action choices. This often leads to good performance with some
teammates and inferior performance with other teammates, as the agent cannot
adapt to any specific teammate. In this paper we present the first adaptive
agent for playing Codenames. We adopt an ensemble approach with the goal of
determining, during the course of interacting with a specific teammate, which
of our internal expert agents, each potentially with its own language model, is
the best match. One difficulty faced in this approach is the lack of a single
numerical metric that accurately captures the performance of a Codenames team.
Prior Codenames research has utilized a handful of different metrics to
evaluate agent teams. We propose a novel single metric to evaluate the
performance of a Codenames team, whether playing a single team (solitaire)
game, or a competitive game against another team. We then present and analyze
an ensemble agent which selects an internal expert on each turn in order to
maximize this proposed metric. Experimental analysis shows that this ensemble
approach adapts to individual teammates and often performs nearly as well as
the best internal expert with a teammate. Crucially, this success does not
depend on any previous knowledge about the teammates, the ensemble agents, or
their compatibility. This research represents an important step to making
language-based agents for cooperative language settings like Codenames more
adaptable to individual teammates.