  We propose a method for modeling and learning turn-taking behaviors for
accessing a shared resource. We model the individual behavior for each agent in
an interaction and then use a multi-agent fusion model to generate a summary
over the expected actions of the group to render the model independent of the
number of agents. The individual behavior models are weighted finite state
transducers (WFSTs) with weights dynamically updated during interactions, and
the multi-agent fusion model is a logistic regression classifier.
  We test our models in a multi-agent tower-building environment, where a
Q-learning agent learns to interact with rule-based agents. Our approach
accurately models the underlying behavior patterns of the rule-based agents
with accuracy ranging between 0.63 and 1.0 depending on the stochasticity of
the other agent behaviors. In addition we show using KL-divergence that the
model accurately captures the distribution of next actions when interacting
with both a single agent (KL-divergence < 0.1) and with multiple agents
(KL-divergence < 0.37). Finally, we demonstrate that our behavior model can be
used by a Q-learning agent to take turns in an interactive turn-taking
environment.
