Event perception refers to people's ability to carve up continuous experience into meaningful discrete events. We speak of finishing our morning coffee, mowing the lawn, leaving work, etc. as singular occurrences that are localized in time and space. In this work, we analyze how spatiotemporal representations can be used to automatically segment continuous experience into structured episodes, and how these descriptions can be used for analogical learning. These representations are based on Hayes' notion of histories and build upon existing work on qualitative episodic memory. Our agent automatically generates event descriptions of military battles in a strategy game and improves its gameplay by learning from this experience. Episodes are segmented based on changing properties in the world and we show evidence that they facilitate learning because they capture event descriptions at a useful spatiotemporal grain size. This is evaluated through our agent's performance in the game. We also show empirical evidence that the perception of spatial extent of episodes affects both their temporal duration as well as the number of overall cases generated.

翻译：事件感知指的是人们将连续经验划分为有意义离散事件的能力。我们将完成晨间咖啡、修剪草坪、下班等描述为在时间和空间上具有定位性的单一事件。在本研究中，我们分析了如何利用时空表征将连续经验自动分割为结构化情景片段，以及如何将这些描述用于类比学习。这些表征基于Hayes的历史概念，并建立在现有定性情景记忆研究的基础上。我们的智能体能够自动生成策略游戏中军事战斗的事件描述，并通过从这些经验中学习来提升游戏表现。情景片段根据世界中变化的属性进行分割，我们提供的证据表明，由于这些片段在有用的时空粒度上捕捉事件描述，从而促进了学习过程。这一结论通过智能体在游戏中的表现进行评估。我们还通过实证证据表明，对情景空间范围的感知既会影响其时间持续时间，也会影响生成案例的总数量。