Recent advancements in LLMs have contributed to the rise of advanced conversational assistants that can assist with user needs through natural language conversation. This paper presents a ScheduleMe, a multi-agent calendar assistant for users to manage google calendar events in natural language. The system uses a graph-structured coordination mechanism where a central supervisory agent supervises specialized task agents, allowing modularity, conflicts resolution, and context-aware interactions to resolve ambiguities and evaluate user commands. This approach sets an example of how structured reasoning and agent cooperation might convince operators to increase the usability and flexibility of personal calendar assistant tools.
Keywords: Natural Language Processing | Sinhala | Multi-agent | LLM |
@inproceedings{wijerathne2025scheduleme,
title={{ScheduleMe: Multi-Agent Calendar Assistant}},
author={Wijerathne, Oshadha and Nimasha, Amandi and Fernando, Dushan and de Silva, Nisansa and Perera, Srinath},
booktitle={Proceedings of the 39th Pacific Asia Conference on Language, Information and Computation},
year={2025},
abstract={Recent advancements in LLMs have contributed to the rise of advanced conversational assistants that can assist with user needs through natural language conversation. This paper presents a ScheduleMe, a multi-agent calendar assistant for users to manage google calendar events in natural language. The system uses a graph-structured coordination mechanism where a central supervisory agent supervises specialized task agents, allowing modularity, conflicts resolution, and context-aware interactions to resolve ambiguities and evaluate user commands. This approach sets an example of how structured reasoning and agent cooperation might convince operators to increase the usability and flexibility of personal calendar assistant tools.},
pages={309--319},
misc={https://aclanthology.org/2025.paclic-1.27/,NLP:SIN,https://goo.gl/iY6aTr}
}
ActivePI: Nisansa de Silva
The LLM Multi-agent Systems project explores how large language models can be coordinated as interacting agents to perform complex reasoning, cooperation, and task execution in natural language environments.