  Ensuring artificial intelligence behaves in such a way that is aligned with
human values is commonly referred to as the alignment challenge. Prior work has
shown that rational agents, behaving in such a way that maximizes a utility
function, will inevitably behave in such a way that is not aligned with human
values, especially as their level of intelligence goes up. Prior work has also
shown that there is no "one true utility function"; solutions must include a
more holistic approach to alignment. This paper describes oblivious agents:
agents that are architected in such a way that their effective utility function
is an aggregation of a known and hidden sub-functions. The hidden component, to
be maximized, is internally implemented as a black box, preventing the agent
from examining it. The known component, to be minimized, is knowledge of the
hidden sub-function. Architectural constraints further influence how agent
actions can evolve its internal environment model. We show that an oblivious
agent, behaving rationally, constructs an internal approximation of designers'
intentions (i.e., infers alignment), and, as a consequence of its architecture
and effective utility function, behaves in such a way that maximizes alignment;
i.e., maximizing the approximated intention function. We show that,
paradoxically, it does this for whatever utility function is used as the hidden
component and, in contrast with extant techniques, chances of alignment
actually improve as agent intelligence grows.
