no code implementations • 13 Mar 2024 • Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, QIngwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong.
no code implementations • 27 Feb 2024 • Kaikai An, Fangkai Yang, Junting Lu, Liqun Li, Zhixing Ren, Hao Huang, Lu Wang, Pu Zhao, Yu Kang, Hua Ding, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Effective incident management is pivotal for the smooth operation of enterprises-level cloud services.
no code implementations • 22 Feb 2024 • Lele Cao, Valentin Buchner, Zineb Senane, Fangkai Yang
We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate.
no code implementations • 13 Jan 2024 • Lu Wang, Mayukh Das, Fangkai Yang, Chao Duo, Bo Qiao, Hang Dong, Si Qin, Chetan Bansal, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.
1 code implementation • 29 Nov 2023 • Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang, Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin, Xiaoting Qin, Chao Du, Yong Xu, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
no code implementations • 24 Oct 2023 • Zezhong Wang, Fangkai Yang, Lu Wang, Pu Zhao, Hongru Wang, Liang Chen, QIngwei Lin, Kam-Fai Wong
Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards.
no code implementations • 9 Oct 2023 • Yinfeng Yu, Changan Chen, Lele Cao, Fangkai Yang, Fuchun Sun
As humans, we hear sound every second of our life.
no code implementations • 3 Aug 2023 • Fangkai Yang, Wenjie Yin, Lu Wang, Tianci Li, Pu Zhao, Bo Liu, Paul Wang, Bo Qiao, Yudong Liu, Mårten Björkman, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance.
no code implementations • 19 May 2023 • Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.
1 code implementation • 19 May 2023 • Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Jue Zhang, Mohit Garg, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge.
no code implementations • 21 Nov 2022 • Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 13 Aug 2021 • Daoming Lyu, Fangkai Yang, Hugh Kwon, Wen Dong, Levent Yilmaz, Bo Liu
Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy.
no code implementations • 18 Sep 2019 • Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson
Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry.
no code implementations • 17 Sep 2019 • Bart Bogaerts, Esra Erdem, Paul Fodor, Andrea Formisano, Giovambattista Ianni, Daniela Inclezan, German Vidal, Alicia Villanueva, Marina De Vos, Fangkai Yang
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.
no code implementations • 10 Aug 2019 • Esra Erdem, Andrea Formisano, German Vidal, Fangkai Yang
We are proud to introduce this special issue of Theory and Practice of Logic Programming (TPLP), dedicated to the regular papers accepted for the 35th International Conference on Logic Programming (ICLP).
no code implementations • 17 Jun 2019 • Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson
Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage.
no code implementations • 6 May 2019 • Alexander Elkholy, Fangkai Yang, Steven Gustafson
Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred.
no code implementations • 21 Nov 2018 • Yuqian Jiang, Fangkai Yang, Shiqi Zhang, Peter Stone
In the outer loop, the plan is executed, and the robot learns from the execution experience via model-free RL, to further improve its task-motion plans.
no code implementations • 31 Oct 2018 • Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson
The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input.
1 code implementation • 16 Oct 2018 • Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge.
no code implementations • 20 Apr 2018 • Fangkai Yang, Daoming Lyu, Bo Liu, Steven Gustafson
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents.
no code implementations • 20 Dec 2013 • Amelia Harrison, Vladimir Lifschitz, Fangkai Yang
Input languages of answer set solvers are based on the mathematically simple concept of a stable model.