Imagine an AI navigating a virtual world described entirely through text, like an old-school text adventure game. It needs to figure out how to achieve goals, like finding a hidden coin, with only limited information about its surroundings. This is the challenge tackled by researchers in a new paper introducing PDDLEGO, an innovative approach to planning in these textual environments. Traditional AI planning methods often struggle in these scenarios because they need a complete picture of the world upfront. PDDLEGO gets around this by iteratively building its understanding. It starts with a basic plan based on what it knows, explores a bit, updates its knowledge, and refines its plan. Think of it like building with LEGOs. You start with a few blocks, add more as you go, and eventually create something complex. PDDLEGO does the same with its plan, adding new actions as it discovers more about the virtual world. This iterative approach is significantly more efficient than previous methods, especially in complex environments like a virtual cooking game. In tests, PDDLEGO completed tasks with 43% fewer steps than other AI agents. It also proved more robust, consistently finding solutions even when the virtual world was randomized. This research opens exciting possibilities for creating more intelligent and adaptable AI agents that can navigate and solve problems in complex, dynamic environments. While the current version of PDDLEGO relies on some pre-defined knowledge about the world, future research aims to make it even more flexible and adaptable, allowing it to learn and plan in completely unknown environments.