用逆向推理解决Minecraft复杂任务，提升规划效率与成功率
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks
- 从目标反推步骤，采用逆向推理规划
- 在Minecraft中任务成功率提升至87.3%
- 适合需要长程规划的智能体研究者
基于大语言模型的智能体在遵循人类指令和自动完成任务方面展现出巨大潜力。为完成任务，智能体需通过规划将任务分解为可执行步骤。现有方法主要采用从初始状态出发预测下一步的正向推理，但在复杂任务中表现不佳。我们以模拟现实场景的Minecraft虚拟环境为研究场景，认为正向推理失败源于初始状态与任务目标间的巨大感知差距。为此，我们提出基于逆向推理的规划方法，从终态出发，一步直达目标。具体设计了基于逆向推理的智能体（BAR），包含递归目标分解模块、状态一致性保持模块和阶段记忆模块，实现从终态出发的鲁棒、一致且高效的规划。实验结果表明，BAR在多项复杂任务上显著优于现有方法，所提模块有效提升了性能。
原文摘要 · Abstract (English)
Large language model (LLM) based agents have shown great potential in following human instructions and automatically completing various tasks. To complete a task, the agent needs to decompose it into easily executed steps by planning. Existing studies mainly conduct the planning by inferring what steps should be executed next starting from the agent's initial state. However, this forward reasoning paradigm doesn't work well for complex tasks. We propose to study this issue in Minecraft, a virtual environment that simulates complex tasks based on real-world scenarios. We believe that the failure of forward reasoning is caused by the big perception gap between the agent's initial state and task goal. To this end, we leverage backward reasoning and make the planning starting from the terminal state, which can directly achieve the task goal in one step. Specifically, we design a BAckward Reasoning based agent (BAR). It is equipped with a recursive goal decomposition module, a state consistency maintaining module and a stage memory module to make robust, consistent, and efficient planning starting from the terminal state. Experimental results demonstrate the superiority of BAR over existing methods and the effectiveness of proposed modules.
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