Planning

TravelAgent: An AI Assistant for Personalized Travel Planning

We introduce TravelAgent, an LLM-powered travel planning system that generates rational, comprehensive, and personalized itineraries through four modules, demonstrating effectiveness in dynamic scenarios.

Aili Chen, Xuyang Ge, Ziquan Fu, Yanghua Xiao, Jiangjie Chen

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

We introduce EvoAgent, a method using evolutionary algorithms to automatically expand expert agents into multi-agent systems, enhancing the task-solving capabilities of large language model-based agents without additional human design.

Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals

We introduce SelfGoal, an automatic approach that enhances language agents’ capabilities to achieve high-level goals with limited instructions and delayed feedback by adaptively breaking down goals into practical subgoals.

Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang

SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation

TimeArena enhances LLMs with temporal dynamics for better multitasking, showing advanced models like GPT-4 still trail behind human temporal awareness.

Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation

TravelPlanner: A Benchmark for Real-World Planning with Language Agents

We introduced TravelPlanner, a benchmark for assessing language agents’ planning abilities, showing that even advanced models like GPT-4 face difficulties with complex tasks.

Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su

TravelPlanner: A Benchmark for Real-World Planning with Language Agents

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

We propose AucArena to tests LLMs in auctions, showing they can strategize but with variable success, indicating potential for enhancement.

Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, Kyle Richardson

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

Distilling Script Knowledge from Large Language Models for Constrained Language Planning

We propose an over-generate-then-filter approach to improve large language models (LLMs) on constrained language planning, and use it to distill a novel constrained language planning dataset, CoScript.

Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Robert Jankowski, Yanghua Xiao, Deqing Yang

Distilling Script Knowledge from Large Language Models for Constrained Language Planning