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.

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods - word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases.

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods - word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases.