In this work, we introduce a novel approach that equips LLM agents with
introspection, enhancing consistency and adaptability in solving complex tasks.
Our approach prompts LLM agents to decompose a given task into manageable
subtasks (i.e., to make a plan), and to continuously introspect upon the
suitability and results of their actions. We implement a three-fold
introspective intervention: 1) anticipatory reflection on potential failures
and alternative remedy before action execution, 2) post-action alignment with
subtask objectives and backtracking with remedy to ensure utmost effort in plan
execution, and 3) comprehensive review upon plan completion for future strategy
refinement. By deploying and experimenting with this methodology - a zero-shot
approach - within WebArena for practical tasks in web environments, our agent
demonstrates superior performance over existing zero-shot methods. The
experimental results suggest that our introspection-driven approach not only
enhances the agent's ability to navigate unanticipated challenges through a
robust mechanism of plan execution, but also improves efficiency by reducing
the number of trials and plan revisions needed to achieve a task.

我们介绍了一种新颖的方法，为 LLM 智能体提供自省能力，以增强其在解决复杂任务中的一致性和适应性。我们的方法促使 LLM 智能体将给定的任务分解为可管理的子任务（即制定计划），并持续对其行动的适用性和结果进行自省。通过在 Web 环境中使用这种零样本方法进行实际任务的部署和实验，我们的智能体展示了优于现有零样本方法的性能。实验结果表明，我们的自省驱动方法不仅通过稳定的计划执行机制增强了智能体应对意外挑战的能力，而且通过减少试验和计划修订所需的次数，提高了任务的效率。

魔鬼的辩护：面向 LLM 代理的预期反思

Devil's Advocate: Anticipatory Reflection for LLM Agents

Autonomous agents acting in real-world environments often need to reason with
unknown novelties interfering with their plan execution. Novelty is an
unexpected phenomenon that can alter the core characteristics, composition, and
dynamics of the environment. Novelty can occur at any time in any sufficiently
complex environment without any prior notice or explanation. Previous studies
show that novelty has catastrophic impact on agent performance. Intelligent
agents reason with an internal model of the world to understand the intricacies
of their environment and to successfully execute their plans. The introduction
of novelty into the environment usually renders their internal model inaccurate
and the generated plans no longer applicable. Novelty is particularly prevalent
in the real world where domain-specific and even predicted novelty-specific
approaches are used to mitigate the novelty's impact. In this work, we
demonstrate that a domain-independent AI agent designed to detect,
characterize, and accommodate novelty in smaller-scope physics-based games such
as Angry Birds and Cartpole can be adapted to successfully perform and reason
with novelty in realistic high-fidelity simulator of the military domain.

本文旨在探索如何让自主智能系统在真实世界的环境中适应、解决并执行计划，针对其中出现的未知问题和新的情况需要进行建模和检测。

高保真模拟开放世界中的新颖性适应多智能体规划

Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated  Open World

This paper presents a novel approach for learning STRIPS action models from
examples that compiles this inductive learning task into a classical planning
task. Interestingly, the compilation approach is flexible to different amounts
of available input knowledge; the learning examples can range from a set of
plans (with their corresponding initial and final states) to just a pair of
initial and final states (no intermediate action or state is given). Moreover,
the compilation accepts partially specified action models and it can be used to
validate whether the observation of a plan execution follows a given STRIPS
action model, even if this model is not fully specified.

本文提出了一种新颖的方法，该方法从示例中学习 STRIPS 行动模型，并将其编译为一个经典的规划任务。编译方法对可用输入知识的不同量具有灵活性，并且可以接受部分指定的行动模型，并且可以用于验证计划执行的观察是否遵循给定的 STRIPS 行动模型，即使该模型不是完全指定。