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.

翻译：学习检测、表征及适应新奇性，是运行于开放世界中的智能体为确保满意任务执行效果而必须应对的挑战。某些新奇性（如环境动态变化）可能干扰智能体性能，甚至阻碍其达成任务目标。本文提出了用于检测和表征不同类型新奇性的通用方法与架构机制，并构建了基于逻辑表示与推理方法的自适应模型以应对这些变化。我们通过第三方在对抗性多智能体棋盘游戏Monopoly中进行的评估实验，验证了所提方法的有效性。结果表明，该方法在多种新奇类型（包括游戏规则变更及智能体行为能力变化）下均展现出较高的新奇检测率与适应成功率。