Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue
Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue
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DOI：
10.24963/ijcai.2023/330
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Cristian-Paul Bara;Ziqiao Ma;Yingzhuo Yu;J. Shah;J. Chai
中科院分区：
文献类型：
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作者：
Cristian-Paul Bara;Ziqiao Ma;Yingzhuo Yu;J. Shah;J. Chai
协作任务通常从每个合作伙伴的部分任务知识和不完整的计划开始。为了完成这些任务，合作伙伴需要与他们的合作伙伴进行情境沟通，并将他们的部分计划协调成一个完整的计划，以实现共同的任务目标。虽然这种合作在人与人之间的团队中似乎毫不费力，但在人类与人工智能的合作中却是极具挑战性的。为了解决这一限制，本文向协作计划获取迈出了一步，在协作计划获取中，人类和智能体努力相互学习和交流，以获取联合任务的完整计划。具体来说，我们提出了一个新的问题，让智能体基于丰富的感知和对话历史，为自己和伙伴预测缺失的任务知识。我们扩展了三维块世界中对称协作任务的位置对话基准，并研究了计划获取的计算策略。我们的实证结果表明，预测伴侣的知识缺失比预测自己的知识缺失更可行。我们的研究表明，对合作伙伴的对话动作和精神状态进行明确的建模，会比不进行建模产生更好、更稳定的结果。这些结果为未来的人工智能代理提供了洞察力，可以预测他们的合作伙伴缺少什么知识，因此可以主动交流这些信息，帮助合作伙伴获得这些缺失的知识，以共同理解联合任务。
Collaborative tasks often begin with partial task knowledge and incomplete plans from each partner. To complete these tasks, partners need to engage in situated communication with their partners and coordinate their partial plans towards a complete plan to achieve a joint task goal. While such collaboration seems effortless in a human-human team, it is highly challenging for human-AI collaboration. To address this limitation, this paper takes a step towards Collaborative Plan Acquisition, where humans and agents strive to learn and communicate with each other to acquire a complete plan for joint tasks. Specifically, we formulate a novel problem for agents to predict the missing task knowledge for themselves and for their partners based on rich perceptual and dialogue history. We extend a situated dialogue benchmark for symmetric collaborative tasks in a 3D blocks world and investigate computational strategies for plan acquisition. Our empirical results suggest that predicting the partner's missing knowledge is a more viable approach than predicting one's own. We show that explicit modeling of the partner's dialogue moves and mental states produces improved and more stable results than without. These results provide insight for future AI agents that can predict what knowledge their partner is missing and, therefore, can proactively communicate such information to help the partner acquire such missing knowledge toward a common understanding of joint tasks.