Planning in textual environments have been shown to be a long-standing
challenge even for current models. A recent, promising line of work uses LLMs
to generate a formal representation of the environment that can be solved by a
symbolic planner. However, existing methods rely on a fully-observed
environment where all entity states are initially known, so a one-off
representation can be constructed, leading to a complete plan. In contrast, we
tackle partially-observed environments where there is initially no sufficient
information to plan for the end-goal. We propose PDDLEGO that iteratively
construct a planning representation that can lead to a partial plan for a given
sub-goal. By accomplishing the sub-goal, more information is acquired to
augment the representation, eventually achieving the end-goal. We show that
plans produced by few-shot PDDLEGO are 43% more efficient than generating plans
end-to-end on the Coin Collector simulation, with strong performance (98%) on
the more complex Cooking World simulation where end-to-end LLMs fail to
generate coherent plans (4%).

PDDLEGO proposes an iterative planning representation approach for partially-observed environments, achieving efficient plans and strong performance compared to end-to-end LLMs.

PDDLEGO: 文本环境中的迭代规划

PDDLEGO: Iterative Planning in Textual Environments

In this extended abstract we discuss the opportunities and challenges of
studying intrinsically-motivated agents for exploration in textual
environments. We argue that there is important synergy between text
environments and autonomous agents. We identify key properties of text worlds
that make them suitable for exploration by autonmous agents, namely, depth,
breadth, progress niches and the ease of use of language goals; we identify
drivers of exploration for such agents that are implementable in text worlds.
We discuss the opportunities of using autonomous agents to make progress on
text environment benchmarks. Finally we list some specific challenges that need
to be overcome in this area.

文中探讨了研究本质激励代理在文本环境中探索的机遇和挑战，并认为文本环境及自主代理之间具有重要的协同作用，提出文本世界适合自主代理探索的关键特性包括深度、广度、进步利基以及语言目标易用性，认为实现这些驱动器对可实现文本世界探索自主代理是有帮助的，并列举了需要在该领域克服的一些具体挑战。