From Dialogue to Decision: Orchestrating Agents for Conversational Data Exploration and Multi-Objective Optimization
Abstract:
Modern scientific discovery and decision-making necessitate agents capable of navigating both complex datasets and multifaceted, often conflicting, goal landscapes. This talk explores the intersection of conversational data exploration, an incremental process where agents help users articulate needs through data interaction, and multi-objective sequential decision making. While traditional Reinforcement Learning (RL) has advanced task-specific agent training, we are witnessing a paradigm shift toward zero-shot orchestration. By leveraging a spectrum of techniques, from specialized RL policies to general-purpose Large Language Models (LLMs), we can reuse existing single-objective policies to solve complex multi-objective problems without training from scratch. We will discuss empirical evidence from the field of Education showing that this orchestrated approach can achieve competitive Pareto quality while reducing computational cost. The talk concludes by examining open research questions regarding how LLM context richness and reflective foresight enable agents to bridge the gap between incremental data-driven insights and optimal sequential actions.
Biography:
Biography: Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and algorithmic upskilling. Prior to that she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and as the coordinator of the Diversity, Equity and Inclusion initiative for the database community. In 2024, she received the 2024 IEEE TCDE Impact Award, the SIGMOD Contributions Award, and the VLDB Women in Database Award.