Abstract
As autonomous AI agents proliferate in the real
world, they will increasingly need to cooperate
with each other to achieve complex goals without always being able to coordinate in advance.
This kind of cooperation, in which agents have to
learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to
one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be
guaranteed in many real-world settings. In this
work, we relax this assumption and investigate settings in which teammates can change their types
during the course of the task. This adds complexity to the planning problem as now an agent
needs to recognize that a change has occurred in
addition to figuring out what is the new type of
the teammate it is interacting with. In this paper,
we present a novel Convolutional-Neural-Networkbased Change Point Detection (CPD) algorithm for
ad hoc teamwork. When evaluating our algorithm
on the modified predator prey domain, we find that
it outperforms existing Bayesian CPD algorithms