2,545 research outputs found
Deep Reinforcement Learning Approaches for Technology Enhanced Learning
Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives.
In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS.
However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS.
This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems.
To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training.
Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively.
Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings
Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue
Collaborative tasks often begin with partial task knowledge and incomplete
initial plans from each partner. To complete these tasks, agents need to engage
in situated communication with their partners and coordinate their partial
plans towards a complete plan to achieve a joint task goal. While such
collaboration seems effortless in a human-human team, it is highly challenging
for human-AI collaboration. To address this limitation, this paper takes a step
towards collaborative plan acquisition, where humans and agents strive to learn
and communicate with each other to acquire a complete plan for joint tasks.
Specifically, we formulate a novel problem for agents to predict the missing
task knowledge for themselves and for their partners based on rich perceptual
and dialogue history. We extend a situated dialogue benchmark for symmetric
collaborative tasks in a 3D blocks world and investigate computational
strategies for plan acquisition. Our empirical results suggest that predicting
the partner's missing knowledge is a more viable approach than predicting one's
own. We show that explicit modeling of the partner's dialogue moves and mental
states produces improved and more stable results than without. These results
provide insight for future AI agents that can predict what knowledge their
partner is missing and, therefore, can proactively communicate such information
to help their partner acquire such missing knowledge toward a common
understanding of joint tasks
Exploring Human-Robot interaction in Collaborative Tasks
In this project I performed independent research in the field of Human-Robot Collaboration. In doing so, I divided the project into two sub-problems: motion segmentation, and motion planning in the presence of a human. I present an effective method for automatic segmentation of human grasping motions as well as a novel cost function that aims to minimize robotic interference to a human collaborator\u27s workspace
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration
We present a method for learning a human-robot collaboration policy from
human-human collaboration demonstrations. An effective robot assistant must
learn to handle diverse human behaviors shown in the demonstrations and be
robust when the humans adjust their strategies during online task execution.
Our method co-optimizes a human policy and a robot policy in an interactive
learning process: the human policy learns to generate diverse and plausible
collaborative behaviors from demonstrations while the robot policy learns to
assist by estimating the unobserved latent strategy of its human collaborator.
Across a 2D strategy game, a human-robot handover task, and a multi-step
collaborative manipulation task, our method outperforms the alternatives in
both simulated evaluations and when executing the tasks with a real human
operator in-the-loop. Supplementary materials and videos at
https://sites.google.com/view/co-gail-web/homeComment: CoRL 202
Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot Collaboration
We study human-robot handovers in a naturalistic collaboration scenario,
where a mobile manipulator robot assists a person during a crafting session by
providing and retrieving objects used for wooden piece assembly (functional
activities) and painting (creative activities). We collect quantitative and
qualitative data from 20 participants in a Wizard-of-Oz study, generating the
Functional And Creative Tasks Human-Robot Collaboration dataset (the FACT HRC
dataset), available to the research community. This work illustrates how social
cues and task context inform the temporal-spatial coordination in human-robot
handovers, and how human-robot collaboration is shaped by and in turn
influences people's functional and creative activities.Comment: accepted at HRI 202
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