As advances in artificial intelligence enable increasingly capable learning-based autonomous agents, it becomes more challenging for human observers to efficiently construct a mental model of the agent's behaviour. In order to successfully deploy autonomous agents, humans should not only be able to understand the individual limitations of the agents but also have insight on how they compare against one another. To do so, we need effective methods for generating human interpretable agent behaviour summaries. Single agent behaviour summarization has been tackled in the past through methods that generate explanations for why an agent chose to pick a particular action at a single timestep. However, for complex tasks, a per-action explanation may not be able to convey an agents global strategy. As a result, researchers have looked towards multi-timestep summaries which can better help humans assess an agents overall capability. More recently, multi-step summaries have also been used for generating contrasting examples to evaluate multiple agents. However, past approaches have largely relied on unstructured search methods to generate summaries and require agents to have a discrete action space. In this paper we present an adaptive search method for efficiently generating contrasting behaviour summaries with support for continuous state and action spaces. We perform a user study to evaluate the effectiveness of the summaries for helping humans discern the superior autonomous agent for a given task. Our results indicate that adaptive search can efficiently identify informative contrasting scenarios that enable humans to accurately select the better performing agent with a limited observation time budget.

翻译：随着人工智能的进步使得基于学习的自主智能体能力日益增强，人类观察者越来越难以高效构建智能体行为的心理模型。为了成功部署自主智能体，人类不仅需要理解单个智能体的局限性，还应洞察它们之间的性能对比。为此，我们需要有效的方法来生成人类可理解的智能体行为摘要。过去，单个智能体行为摘要的研究主要通过逐时刻解释智能体为何选择特定动作的方法来实现。然而，对于复杂任务而言，逐动作的解释可能无法传达智能体的全局策略。因此，研究人员转向多时间步摘要，以更好地帮助人类评估智能体的整体能力。最近，多步摘要还被用于生成对比示例以评估多个智能体。然而，以往的方法主要依赖非结构化搜索来生成摘要，并要求智能体具有离散动作空间。本文提出了一种自适应搜索方法，能够高效生成支持连续状态和动作空间的对比行为摘要。我们通过用户研究评估了这些摘要对人类在给定任务中辨别更优自主智能体的有效性。结果表明，自适应搜索能够高效识别具有信息量的对比场景，使人类在有限观察时间预算内准确选择性能更优的智能体。