  Value alignment problems arise in scenarios where the specified objectives of
an AI agent don't match the true underlying objective of its users. The problem
has been widely argued to be one of the central safety problems in AI.
Unfortunately, most existing works in value alignment tend to focus on issues
that are primarily related to the fact that reward functions are an unintuitive
mechanism to specify objectives. However, the complexity of the objective
specification mechanism is just one of many reasons why the user may have
misspecified their objective. A foundational cause for misalignment that is
being overlooked by these works is the inherent asymmetry in human expectations
about the agent's behavior and the behavior generated by the agent for the
specified objective. To address this lacuna, we propose a novel formulation for
the value alignment problem, named goal alignment that focuses on a few central
challenges related to value alignment. In doing so, we bridge the currently
disparate research areas of value alignment and human-aware planning.
Additionally, we propose a first-of-its-kind interactive algorithm that is
capable of using information generated under incorrect beliefs about the agent,
to determine the true underlying goal of the user.
