Games of Knightian Uncertainty as AGI testbeds

TL;DR

This paper advocates for using games as testbeds for AGI by focusing on Knightian uncertainty, where agents must adapt to sudden, unpredictable rule changes without prior data or models.

Contribution

It introduces the concept of Knightian uncertainty in game environments as a new avenue for testing and developing general intelligence in AI systems.

Findings

Highlights the gap in current AI game research regarding unpredictable rule changes

Proposes a framework for incorporating Knightian uncertainty into game-based AGI testing

Suggests that addressing Knightian uncertainty could accelerate progress toward AGI

Abstract

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.