Semantic Classification of Utterances in a Language-Driven Game

Artificial agents that interact with humans may find that understanding those humans’ plans and goals can improve their interactions. Ideally, humans would explicitly provide information about their plans, goals, and motivations to the agent. However, if the human is unable or unwilling to provide this information then the agent will need to infer it from observed behavior. We describe a goal reasoning agent architecture that allows an agent to classify natural language utterances, hypothesize about human’s actions, and recognize their plans and goals. In this paper we focus on one module of our architecture, the Natural Language Classifier, and demonstrate its use in a multiplayer tabletop social deception game, One Night Ultimate Werewolf. Our evaluation indicates that our system can obtain reasonable performance even when the utterances are unstructured, deceptive, or ambiguous.

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