System Description
System descriptions encompass the design, implementation, and evaluation of computational systems addressing diverse challenges. Current research focuses on improving system efficiency and accuracy through techniques like hybrid neural networks for optimal control, fine-tuned BERT models for question answering, and various large language model (LLM) applications for tasks ranging from automatic scoring to creative idea generation. These advancements are significant for improving automation in various fields, from energy management and disaster response to healthcare and education, and for advancing our understanding of AI capabilities and limitations.
Papers
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, James Pine, Hubert Pham, Ambarish Jash, Sukhdeep Sodhi
SERN: Simulation-Enhanced Realistic Navigation for Multi-Agent Robotic Systems in Contested Environments
Jumman Hossain, Emon Dey, Snehalraj Chugh, Masud Ahmed, MS Anwar, Abu-Zaher Faridee, Jason Hoppes, Theron Trout, Anjon Basak, Rafidh Chowdhury, Rishabh Mistry, Hyun Kim, Jade Freeman, Niranjan Suri, Adrienne Raglin, Carl Busart, Timothy Gregory, Anuradha Ravi, Nirmalya Roy
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting
Towards Autonomous Indoor Parking: A Globally Consistent Semantic SLAM System and A Semantic Localization Subsystem
Yichen Sha, Siting Zhu, Hekui Guo, Zhong Wang, Hesheng Wang
Generative AI and Its Impact on Personalized Intelligent Tutoring Systems
Subhankar Maity, Aniket Deroy
Compositional Shielding and Reinforcement Learning for Multi-Agent Systems
Asger Horn Brorholt, Kim Guldstrand Larsen, Christian Schilling
XAI-based Feature Selection for Improved Network Intrusion Detection Systems
Osvaldo Arreche, Tanish Guntur, Mustafa Abdallah