  As humans interact with autonomous agents to perform increasingly
complicated, potentially risky tasks, it is important to be able to efficiently
evaluate an agent's performance and correctness. In this paper we formalize and
theoretically analyze the problem of efficient value alignment verification:
how to efficiently test whether the behavior of another agent is aligned with a
human's values. The goal is to construct a kind of "driver's test" that a human
can give to any agent which will verify value alignment via a minimal number of
queries. We study alignment verification problems with both idealized humans
that have an explicit reward function as well as problems where they have
implicit values. We analyze verification of exact value alignment for rational
agents and propose and analyze heuristic and approximate value alignment
verification tests in a wide range of gridworlds and a continuous autonomous
driving domain. Finally, we prove that there exist sufficient conditions such
that we can verify exact and approximate alignment across an infinite set of
test environments via a constant-query-complexity alignment test.
