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 …

Large language models (LLMs) have demonstrated remarkable zero-shot generalization
abilities: state-of-the-art chatbots can provide plausible answers to many common questions …

In recent years, there has been remarkable progress in leveraging Language Models (LMs),
encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models …

Grounding the reasoning ability of large language models (LLMs) for embodied tasks is
challenging due to the complexity of the physical world. Especially, LLM planning for multi …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Recent advances in task planning leverage Large Language Models (LLMs) to improve
generalizability by combining such models with classical planning algorithms to address …

Multi-modal foundation models and generative AI have demonstrated promising capabilities
in applications across various domains. Recently, Vision-language-action (VLA) models …

In the realm of data-driven AI technology, the application of open-source large language
models (LLMs) in robotic task planning represents a significant milestone. Recent robotic …

While the exploration for embodied AI has spanned multiple decades, it remains a persistent
challenge to endow agents with human-level intelligence, including perception, learning …

We introduce tulip agent, an architecture for autonomous LLM-based agents with Create,
Read, Update, and Delete access to a tool library containing a potentially large number of …