This project aims to design a hierarchical cross-network multi-agent Reinforcement-Learning-based trading strategy generator and examines governance framework for crypto asset markets.
This proposal contributes to Thrust 3 of the National Quantum Computing Hub (NQCH) that is focused on translational R&D, such as the development of libraries, prebuild models, and templates to enable easier and faster programming and developments of software applications by early adopters in the industry, government agencies and Institutes of Higher Learning (IHLs). This project aims to develop hybrid quantum-classical algorithms and tools that will contribute to the libraries and pre-build models for supply chain use cases. Compared with classical techniques, we aim to enhance the performance of the Sample Average Approximation (SAA) and Simulation Optimization, that is verifiable in today’s NISQ quantum hardware, and apply these algorithms to supply chain risk management contexts. It is anticipated that these algorithms will achieve higher-quality and computationally attractive solutions over pure classical algorithms.
This is a project under the AI Singapore 100 Experiments (Research) Programme. BIPO has a unique advantage in payroll processing and saw an opportunity to build a tool anchoring on downstream pay outcomes as an enabler in strategic design of a rostering tool, that should not only feedback about staff costs, productivity, and preferences, but also feedback on skills-based job evaluation and design. BIPO’s client pool in labour-intensive industries such as logistics, retail (restaurants, shops), call centers, healthcare and hospitality have an acute need for a rostering tool that is based on roles, skills and pay. In this project, we combine constraint programming with adaptive large neighborhood search to generate rosters according to rostering requirement and maximizing the preferences of employees. We also cover the dynamic setting where reinforcement learning is applied to prescribe changes to the roster due to changes in the environment.
This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-100E-2022-098).
This project studies a way to efficiently bootstrap graph neural networks (GNN), a deep learning technique on graphs. A graph (also called network) contains different entities, which are further linked based on their interactions, to form complex networks. However, to achieve optimal performance, for each graph and analytics task, GNNs require a large amount of task-specific labels, which are example cases happened in the past. Such labels are often unavailable or expensive to collect in large scale. In contrast, label-free graphs (i.e., graphs without task-specific labels) are more readily available in various domains. To overcome this critical limitation, the project team turn to GNN pre-training, which can efficiently bootstrap GNNs using label-free graphs and only a small amount of task-specific labels, to capture intrinsic graph properties that can be generalized across tasks and graphs in a domain. Practical applications of this research include fraud detection and anti-money laundry on financial networks, container demand and shipping prediction on supply chain networks and talent match on job/skill graphs.
Digital wellbeing has arisen in public, governmental and policy discourse as a key measure of a person’s wellbeing through a healthy use of technology. This project aims to identify and measure digital wellbeing for digital readiness, inclusion and safety. Building on the Digital Wellbeing Indicator Framework (DWIF) developed by researchers at the NUS Centre for Trusted Internet and Community, this project will test, evaluate, and revise the DWIF by conducting both qualitative and quantitative analysis of data collected from local context (i.e, Singapore) and global contexts (ie, UK, US, China), with specific focus on mainstream job trends (digital readiness), minority disability access (digital inclusion) and women (digital safety).
A recommender system presents a personalized experience to each user. One perennial issue affecting current recommendation technologies is the sparsity of data related to user preferences. The overall objective of this proposed research is to address this sparsity problem by a combination of approaches that together enable lifelong learning for recommender systems. This is done by allowing the recommendation model to evolve over time to new users and items and, to transfer over to new product categories. In addition, the proposed recommendation model would have the ability to cross from a source platform that accumulates longer-term preferences to a target platform that seeks to integrate short-term signals and reinforcement learning. This provides a system that is able to learn from longer-term preferences and provide the necessary flexibility for cross platform applications.
This research is an ideation/proof of concept project to develop an interactive Web application, called Slide++, which allows students to self-explore additional content related to their courses, while still being directed by the lesson materials provided by an instructor. Importantly, its primary feature is to provide content augmentation for every slide in the form of learning resources relevant to the slide being viewed. These resources can be of various modalities, including Web pages, videos, or questions and answers (Q&A’s).
This project aims to build an Automatic Programming Coaching system that is based on a combination of AI and software engineering techniques to support students practice coding via formative feedback generation.
Rare events, also known as “black swans”, in financial time series can be seen as sporadic and drastic jumps in financial assets returns. Accurate and timely estimates of future risk associated with rare events are of great importance for finance practitioners, policymakers, and regulators. The research team will leverage the most recent developments in quantum-enhanced Monte-Carlo sampling, stochastic modelling and dimensional reduction to design a set of quantum algorithms for rare event estimation that:
1. Enhance the accuracy in estimating the probability of specific rare events – we anticipate a quadratic scaling improvement, where doubling the iterations for the quantum algorithm will result in an accuracy improvement equivalent to a quadrupling of iterations in its classical counterpart.
2. Reduce systematic error caused by dimensional reduction – when constrained to storing the same amount of past data (e.g., macroeconomic indicators), our quantum model can give more accurate rare event predictions than classical counterparts.
This is a project under the AI Singapore 100 Experiments Programme. It tackles the research challenge of generating good logistics plans and schedules for parcel delivery using AI. uParcel faces the challenges that the number of deliveries daily are in the thousands and the number of drivers delivering a day is in hundreds, which makes it very challenging to match jobs to drivers and encourage job acceptance. Using reinforcement learning coupled with large-scale optimization methods, the research team will develop route optimization, dynamic recommendation, and logistics marketplace matching algorithms for improving operational efficiency. This will also greatly improve city logistics by reducing trips and congestion.