Games as Experimental Philosophy: Human and Artificial Learning in ARC-AGI-3 Worlds
How do humans and artificial agents learn to act in a world whose rules they do not yet know? We use LS20, a novel ARC-AGI-3 game, as an artificial world: a microcosm whose mechanics must be discovered through exploration and improvisation alone. We operationalise a methodological loop: observe how humans explore, adapt, and become flexible; extract principles of adaptive coupling; build artificial systems embodying those principles; and let the comparison reveal where the artificial model still diverges. By analysing the completed trajectories of 18 human players, we identify a descriptive bottleneck level for each player: a level that consumes a disproportionate share of total actions, after which many trajectories become more efficient. The clearest temporal effect is that players pause 2.1× longer after actions that change a distal reference pattern, consistent with state checking after a causal intervention. Our current artificial agent also solves LS20, but requires about 2,400 actions across 17 attempts, roughly 3.6× the mean human action count (human range 405–1111 actions). We interpret the gap as a difference in how interaction is organised under uncertainty and resource pressure: human players appear to turn visual differences into action-testable regularities, while the agent still relies on slower explicit probing. We frame these patterns through the protocognition taxonomy (Rodriguez-Vergara and Husbands, 2026) and the bodily mindedness framework (Parvizi-Wayne and Montefiore, 2026), arguing that what emerges is neither mindless flow nor reflective deliberation, but a form of skilful, flexible engagement whose computational underpinnings remain an open empirical question. Data/Code available at: ARC-AGI-3 games: https://three.arcprize.org; project data, agent traces, scripts, and figures are available from the authors upon reasonable request.