  Conversations with non-player characters (NPCs) in games are typically
confined to dialogue between a human player and a virtual agent, where the
conversation is initiated and controlled by the player. To create richer, more
believable environments for players, we need conversational behavior to reflect
initiative on the part of the NPCs, including conversations that include
multiple NPCs who interact with one another as well as the player. We describe
a generative computational model of group conversation between agents, an
abstract simulation of discussion in a small group setting. We define
conversational interactions in terms of rules for turn taking and interruption,
as well as belief change, sentiment change, and emotional response, all of
which are dependent on agent personality, context, and relationships. We
evaluate our model using a parameterized expressive range analysis, observing
correlations between simulation parameters and features of the resulting
conversations. This analysis confirms, for example, that character
personalities will predict how often they speak, and that heterogeneous groups
of characters will generate more belief change.
