Works (6)

Updated: July 31st, 2024 05:03

2024 article

LLM-Based Student Plan Generation for Adaptive Scaffolding in Game-Based Learning Environments

Goslen, A., Kim, Y. J., Rowe, J., & Lester, J. (2024, July 17). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 7.

By: A. Goslen*, Y. Kim*, J. Rowe* & J. Lester*

author keywords: Goal setting and planning; Game-based learning environments; Language models; Self-regulated learning
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 22, 2024

2023 article

Time-aware deep reinforcement learning with multi-temporal abstraction

Kim, Y. J., & Chi, M. (2023, March 25). APPLIED INTELLIGENCE.

By: Y. Kim n & M. Chi n

author keywords: Time-aware; Temporal abstraction; Deep reinforcement learning; Irregular time series; RL for real-world applications; Nuclear reactor control; Sepsis treatment
TL;DR: T-MTA significantly outperforms competing baseline frameworks, including a standalone Time-aware DRL framework, MTAs, and the original DRL methods without considering either type of temporal aspect, especially when partially observable environments are involved and the range of time intervals is large. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: April 11, 2023

2021 article

InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1337–1348.

By: M. Ausin n, H. Azizsoltani n, S. Ju n, Y. Kim n & M. Chi n

author keywords: Credit Assignment Problem; Deep Reinforcement Learning
TL;DR: The results show that InferNet is robust to delayed or noisy reward functions, and it could be used effectively for solving the temporal CAP in a wide range of RL tasks, when immediate rewards are not available or they are noisy. (via Semantic Scholar)
Source: Web Of Science
Added: July 5, 2022

2021 article

Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1657–1663.

By: Y. Kim n, M. Ausin n & M. Chi n

author keywords: deep reinforcement learning; time-aware; temporal abstraction; sepsis
TL;DR: This work proposes MTA-TQN, a Multi-view -Temporal Abstraction mechanism within a Time-aware deep Q-learning Network framework for septic shock prevention and demonstrates that both time-awareness and multi-view temporal abstraction are essential to induce effective policies, particularly with irregular time-series data. (via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being (OpenAlex)
Source: Web Of Science
Added: July 5, 2022

2021 article

To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1640–1646.

By: S. Ju n, Y. Kim n, M. Ausin n, M. Mayorga n & M. Chi n

author keywords: Reinforcement Learning; Sepsis; Critical Decision
TL;DR: The Critical-DRL approach, by which decisions are made at critical junctures, is as effective as a fully executed DRL policy and moreover, it enables the critical moments in the septic treatment process, thus greatly reducing burden on medical decision-makers by allowing them to make critical clinical decisions without negatively impacting outcomes. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: July 5, 2022

2019 article

PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

MULTIMEDIA MODELING (MMM 2020), PT II, Vol. 11962, pp. 63–75.

By: . Yang n, Y. Kim n, M. Taub*, R. Azevedo* & M. Chi n

author keywords: Multimodal; Block-wise missing; Learning gain prediction
TL;DR: A Progressively Refined Imputation for Multi-modalities by auto-Encoder (PRIME), which trains the model based on single, pairwise, and entire modalities for imputation in a progressive manner, and therefore enables us to maximally utilize all the available data. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: February 22, 2021

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