Artificial Intelligence (AI) systems, trained in controlled environments,
often struggle in real-world complexities. We propose a general framework for
estimating domain complexity across diverse environments, like open-world
learning and real-world applications. This framework distinguishes between
intrinsic complexity (inherent to the domain) and extrinsic complexity
(dependent on the AI agent). By analyzing dimensionality, sparsity, and
diversity within these categories, we offer a comprehensive view of domain
challenges. This approach enables quantitative predictions of AI difficulty
during environment transitions, avoids bias in novel situations, and helps
navigate the vast search spaces of open-world domains.

提出了一个通用框架来估计不同环境中领域复杂性，以解决人工智能系统在真实世界中的困难。通过分析维度、稀疏性和多样性，提供了领域挑战的全面视角，以避免偏见，并帮助在开放世界域中进行广泛搜索。

跨领域理解与估计领域复杂性

Understanding and Estimating Domain Complexity Across Domains

Learning to detect, characterize and accommodate novelties is a challenge
that agents operating in open-world domains need to address to be able to
guarantee satisfactory task performance. Certain novelties (e.g., changes in
environment dynamics) can interfere with the performance or prevent agents from
accomplishing task goals altogether. In this paper, we introduce general
methods and architectural mechanisms for detecting and characterizing different
types of novelties, and for building an appropriate adaptive model to
accommodate them utilizing logical representations and reasoning methods. We
demonstrate the effectiveness of the proposed methods in evaluations performed
by a third party in the adversarial multi-agent board game Monopoly. The
results show high novelty detection and accommodation rates across a variety of
novelty types, including changes to the rules of the game, as well as changes
to the agent's action capabilities.

本文介绍了一种应对开放性场景中新颖性挑战的方法，该方法结合了逻辑表示和推理方法，利用通用方法和架构机制检测、表征新颖性，并构建相应的适应性模型进行应对。通过在多智能体博弈中的评估，结果表明所提出的方法在各种新颖性挑战下均表现出高准确率。