  The key for effective interaction in many multiagent applications is to
reason explicitly about the behaviour of other agents, in the form of a
hypothesised behaviour. While there exist several methods for the construction
of a behavioural hypothesis, there is currently no universal theory which would
allow an agent to contemplate the correctness of a hypothesis. In this work, we
present a novel algorithm which decides this question in the form of a
frequentist hypothesis test. The algorithm allows for multiple metrics in the
construction of the test statistic and learns its distribution during the
interaction process, with asymptotic correctness guarantees. We present results
from a comprehensive set of experiments, demonstrating that the algorithm
achieves high accuracy and scalability at low computational costs.
