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

Goal Alignment: A Human-Aware Account of Value Alignment Problem