MINT：最小信息神经符号树用于目标驱动的知识缺口推理和主动提取
MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
AI总结：
本文提出MINT用于解决AI代理在目标驱动规划中主动提取人类输入的问题，通过神经符号树和自博弈优化策略，提升规划性能。
摘要：
基于语言交互的联合规划是人机协同的关键领域。开放世界中的规划问题常涉及不完整信息和未知因素，如涉及对象、人类目标意图等，导致联合规划中的知识缺口。本文考虑发现最优交互策略以主动提取人类输入的问题，提出最小信息神经符号树（MINT）来推理知识缺口的影响，并利用MINT进行自博弈优化AI代理的提取策略和查询。MINT通过将可能的人机交互命题构建为符号树，并咨询神经规划策略以估计由剩余知识缺口导致的规划结果不确定性。最后，利用LLM搜索和总结MINT的推理过程，并制定一组查询以最优提取人类输入，从而获得最佳规划性能。通过考虑具有知识缺口的扩展马尔可夫决策过程家族，分析给定MINT在主动人类提取下的回报保证。在三个涉及未见/未知对象的基准测试中，MINT基于规划的返回接近专家水平，通过每个任务有限数量的问题，实现显著提升的奖励和成功率。
英文摘要：
Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspects of incomplete information and unknowns, e.g., objects involved, human goals/intents -- thus leading to knowledge gaps in joint planning. We consider the problem of discovering optimal interaction strategies for AI agents to actively elicit human inputs in object-driven planning. To this end, we propose Minimal Information Neuro-Symbolic Tree (MINT) to reason about the impact of knowledge gaps and leverage self-play with MINT to optimize the AI agent's elicitation strategies and queries. More precisely, MINT builds a symbolic tree by making propositions of possible human-AI interactions and by consulting a neural planning policy to estimate the uncertainty in planning outcomes caused by remaining knowledge gaps. Finally, we leverage LLM to search and summarize MINT's reasoning process and curate a set of queries to optimally elicit human inputs for best planning performance. By considering a family of extended Markov decision processes with knowledge gaps, we analyze the return guarantee for a given MINT with active human elicitation. Our evaluation on three benchmarks involving unseen/unknown objects of increasing realism shows that MINT-based planning attains near-expert returns by issuing a limited number of questions per task while achieving significantly improved rewards and success rates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
