Agent-based modeling and simulation have evolved as a powerful tool for modeling
complex systems, offering insights into emergent behaviors and interactions among diverse …

Abstract Automated Planning and Scheduling is among the growing areas in Artificial
Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

The recent surge in research interest in applying large language models (LLMs) to decision-
making tasks has flourished by leveraging the extensive world knowledge embedded in …

Achieving human-like planning and control with multimodal observations in an open world is
a key milestone for more functional generalist agents. Existing approaches can handle …

We give a model-based agent that builds a Python program representing its knowledge of
the world based on its interactions with the environment. The world model tries to explain its …

While large language models (LLMs) have demonstrated impressive performance on a
range of decision-making tasks, they rely on simple acting processes and fall short of broad …

In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to
evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to …

Clinicians often rely on data engineers to retrieve complex patient information from
electronic health record (EHR) systems, a process that is both inefficient and time …

Planning for both immediate and long-term benefits becomes increasingly important in
recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning …