Assessing Adaptive World Models in Machines with Novel Games

TL;DR

This paper proposes a new benchmarking framework using novel games to evaluate AI's ability for rapid world model induction, aiming to enhance adaptive learning and generalization akin to human intelligence.

Contribution

It introduces a novel evaluation paradigm based on dynamic, continually changing games to assess AI's adaptive world model learning capabilities.

Findings

Designed key desiderata for novel game construction

Proposed metrics for evaluating rapid world model induction

Aims to inspire future research on adaptive AI evaluation

Abstract

Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction. However, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment. In this Perspective, we provide a view of world model induction drawing on decades of research in cognitive science on how humans learn and adapt so efficiently; we then call for a new evaluation framework for assessing adaptive world models in AI. Concretely, we propose a new benchmarking paradigm based on suites of carefully designed games with genuine, deep and continually refreshing novelty in the underlying game structures -- we refer to this class of games as novel games. We detail key desiderata for constructing these games and propose appropriate metrics to explicitly challenge and evaluate the agent's ability for rapid world model induction. We hope that this new evaluation framework will inspire future evaluation efforts on world models in AI and provide a crucial step towards developing AI systems capable of human-like rapid adaptation and robust generalization -- a critical component of artificial general intelligence.