Zobrazeno 1 - 10
of 27 177
pro vyhledávání: '"multi-agents"'
Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper presents a novel framework, Multi-Agents for Knowledge Augmentation
Externí odkaz:
http://arxiv.org/abs/2411.14637
Large Language Models (LLMs) have achieved impressive results in knowledge-based Visual Question Answering (VQA). However existing methods still have challenges: the inability to use external tools autonomously, and the inability to work in teams. Hu
Externí odkaz:
http://arxiv.org/abs/2412.18351
Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities of LLMs. Inspired by the stru
Externí odkaz:
http://arxiv.org/abs/2411.13932
Autor:
Bao, Zhijie, Liu, Qingyun, Guo, Ying, Ye, Zhengqiang, Shen, Jun, Xie, Shirong, Peng, Jiajie, Huang, Xuanjing, Wei, Zhongyu
In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present the Personalized Intelligent Outpatient Reception
Externí odkaz:
http://arxiv.org/abs/2411.13902
Autor:
Sharan, Medant, Adak, Chandranath
This paper examines the use of classical deep reinforcement learning (DRL) algorithms, DQN, DDQN, and Dueling DQN, in the strategy game So Long Sucker (SLS), a diplomacy-driven game defined by coalition-building and strategic betrayal. SLS poses uniq
Externí odkaz:
http://arxiv.org/abs/2411.11057
This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by ex
Externí odkaz:
http://arxiv.org/abs/2411.07407
Multi-agent systems - systems with multiple independent AI agents working together to achieve a common goal - are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate w
Externí odkaz:
http://arxiv.org/abs/2411.04578
Generative agents have demonstrated impressive capabilities in specific tasks, but most of these frameworks focus on independent tasks and lack attention to social interactions. We introduce a generative agent architecture called ITCMA-S, which inclu
Externí odkaz:
http://arxiv.org/abs/2409.06750
Autor:
Honarvar, Sara, Diaz-Mercado, Yancy
Publikováno v:
2024 American Control Conference
This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent ed
Externí odkaz:
http://arxiv.org/abs/2411.00223
Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks. However, due to the intricacies of graph structures and the inherent limitations of LLMs in handling long text, current approaches often
Externí odkaz:
http://arxiv.org/abs/2410.05130