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、表征并适应新颖性是运行于开放世界领域的智能体为确保任务性能达标而必须应对的挑战。某些新颖性（如环境动态变化）可能干扰智能体性能，甚至使其完全无法实现任务目标。本文提出通用方法与架构机制，用于检测和表征不同类型的新颖性，并利用逻辑表示与推理方法构建合适的自适应模型加以应对。我们通过第三方在对抗性多智能体棋盘游戏《地产大亨》中开展的评估验证了所提方法的有效性。结果表明，该方法在包括游戏规则变更及智能体行动能力改变在内的多种新颖性类型中均展现出较高检测率与适配率。