Measuring Difficulty of Novelty Reaction

TL;DR

This paper introduces a method to quantify the difficulty of adapting AI systems to novel, unexpected changes in the environment, which is crucial for deploying robust AI in real-world open-world scenarios.

Contribution

It proposes a universal approach to measure the difficulty of novelty reaction, enabling systematic training and evaluation of AI robustness to unforeseen changes.

Findings

The method approximates novelty reaction difficulty effectively.

Difficulty measures align with AI agent performance on novelty tasks.

Provides a framework for benchmarking robustness to open-world changes.

Abstract

Current AI systems are designed to solve close-world problems with the assumption that the underlying world is remaining more or less the same. However, when dealing with real-world problems such assumptions can be invalid as sudden and unexpected changes can occur. To effectively deploy AI-powered systems in the real world, AI systems should be able to deal with open-world novelty quickly. Inevitably, dealing with open-world novelty raises an important question of novelty difficulty. Knowing whether one novelty is harder to deal with than another, can help researchers to train their systems systematically. In addition, it can also serve as a measurement of the performance of novelty robust AI systems. In this paper, we propose to define the novelty reaction difficulty as a relative difficulty of performing the known task after the introduction of the novelty. We propose a universal method that can be applied to approximate the difficulty. We present the approximations of the difficulty using our method and show how it aligns with the results of the evaluation of AI agents designed to deal with novelty.