Abstract
We present a sequential Bayesian belief update algorithm for an emotional dialog agent's inference and behavior. This agent's purpose is to collect usage patterns of natural language description of emotions among a community of speakers, a task which can be seen as a type of computational ethnography. We describe our target application, an emotionally-intelligent agent that can ask questions and learn about emotions through playing the emotion twenty questions (EMO20Q) game. We formalize the agent's algorithms mathematically and algorithmically and test our model experimentally in an experiment of 45 human-computer dialogs with a range of emotional words as the independent variable. We found that 44% of these human-computer dialog games are completed successfully, in comparison with earlier work in which human-human dialogs resulted in 85% successful completion on average. Despite being lower than this upper-bound of human performance, especially on difficult emotion words, the subjects rated that the agent's humanity was 6.1 on a 0 to 10 scale. This indicates that the algorithm we present produces realistic behavior, but that issues of data sparsity may remain.