A Deductive Agent Hierarchy: Strategic Reasoning in Codenames

Joseph Bills, Christopher Archibald

Abstract: The game of Codenames has attracted attention recently as it combines natural language and cooperation in a unique way. Codenames is a clue-giving game, where one teammate gives a clue to their partner, who then has to guess from a set of board words, given the clue. To date, the development of computational agents for Codenames has focused on the language aspects of the game. All clue-giving and guessing AI strategies explored thus far are static, basing their decisions only on the current state, without utilizing information obtained from previous clues and guesses. In this paper we present a preliminary exploration of the strategic aspects of agents for Codenames. We describe a deductive agent hierarchy, where agents utilize teammate models to make their decisions. Existing AI strategies for Codenames can be seen as occupying the bottom level of this hierarchy, and we describe general strategies for higher level agents. To explore these strategic ideas we also introduce a novel abstraction of Codenames called Codenums, which which can be seen as Codenames with numbers instead of words. We experimentally evaluate level 1 hierarchical agents in Codenums, demonstrating the potential for higher level strategic reasoning to influence agent design in Codenames.