  Arguably, for the latter part of the late 20th and early 21st centuries,
games have been seen as the drosophila of AI. Games are a set of exciting
testbeds, whose solutions (in terms of identifying optimal players) would lead
to machines that would possess some form of general intelligence, or at the
very least help us gain insights toward building intelligent machines.
Following impressive successes in traditional board games like Go, Chess, and
Poker, but also video games like the Atari 2600 collection, it is clear that
this is not the case. Games have been attacked successfully, but we are nowhere
near AGI developments (or, as harsher critics might say, useful AI
developments!). In this short vision paper, we argue that for game research to
become again relevant to the AGI pathway, we need to be able to address
\textit{Knightian uncertainty} in the context of games, i.e. agents need to be
able to adapt to rapid changes in game rules on the fly with no warning, no
previous data, and no model access.
