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arxiv logo>cs> arXiv:2502.19500
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Computer Science > Artificial Intelligence

arXiv:2502.19500 (cs)
[Submitted on 26 Feb 2025]

Title:Conversational Planning for Personal Plans

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Abstract:The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code generation, to composing essays, to passing STEM and legal exams, to a new paradigm for knowledge search. Besides those short-term use applications, LLMs are increasingly used to help with real-life goals or tasks that take a long time to complete, involving multiple sessions across days, weeks, months, or even years. Thus to enable conversational systems for long term interactions and tasks, we need language-based agents that can plan for long horizons. Traditionally, such capabilities were addressed by reinforcement learning agents with hierarchical planning capabilities. In this work, we explore a novel architecture where the LLM acts as the meta-controller deciding the agent's next macro-action, and tool use augmented LLM-based option policies execute the selected macro-action. We instantiate this framework for a specific set of macro-actions enabling adaptive planning for users' personal plans through conversation and follow-up questions collecting user feedback. We show how this paradigm can be applicable in scenarios ranging from tutoring for academic and non-academic tasks to conversational coaching for personal health plans.
Subjects:Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Cite as:arXiv:2502.19500 [cs.AI]
 (orarXiv:2502.19500v1 [cs.AI] for this version)
 https://doi.org/10.48550/arXiv.2502.19500
arXiv-issued DOI via DataCite

Submission history

From: Konstantina Christakopoulou [view email]
[v1] Wed, 26 Feb 2025 19:04:26 UTC (7,430 KB)
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