  A key challenge in multi-agent systems is the design of intelligent agents
solving real-world tasks in close interaction with other agents (e.g. humans),
thereby being confronted with a variety of behavioral variations and limited
knowledge about the true behaviors of observed agents. The practicability of
existing works addressing this challenge is being limited due to using finite
sets of hypothesis for behavior prediction, the lack of a hypothesis design
process ensuring coverage over all behavioral variations and
sample-inefficiency when modeling continuous behavioral variations. In this
work, we present an approach to this challenge based on a new framework of
Robust Stochastic Bayesian Games (RSBGs). An RSBG defines hypothesis sets by
partitioning the physically feasible, continuous behavior space of the other
agents. It combines the optimality criteria of the Robust Markov Decision
Process (RMDP) and the Stochastic Bayesian Game (SBG) to exponentially reduce
the sample complexity for planning with hypothesis sets defined over continuous
behavior spaces. Our approach outperforms the baseline algorithms in two
experiments modeling time-varying intents and large multidimensional behavior
spaces, while achieving the same performance as a planner with knowledge of the
true behaviors of other agents.
