Resource Registry: Research Study

This research project explores the application of AI and machine learning technqiues in video games, focusing on procedural content generation and AI-driven advice. The research team (RAIL) aims to dynamically generate diverse game content and create intelligent virtual companions that offer personalised guidance and strategies to enhance player experiences. By integrating AI technologies, the research team strives to revolutionise video game desgin and contribte to the advancement of AI in the gaming industry.

Using Machine Learning and AI to predict financial distress of African start-ups. Project is run by the FinTech Hub at Wits.

In this line of work, the research team is interested in techniques that allow an agent to quickly leverage past knowledge to solve new tasks. In particular, the research team focuses on how agents can acquire behaviours that can be combined to generate interesting, novel abilities. One particular focus is applying Boolean operators to learned behaviours to generate probably optimal solutions to new tasks. These approaches are not only human-understandable but also result in a combinatorial explosion in an agent’s abilities, which is key to tackling the multitasking or lifelong learning setting.

(Tshivenda for health) designed and developed at the Institute for Intelligent Systems (IIS), University of Johannesburg (UJ). The device assists in monitoring and analysing the vital health stats of patients remotely (very helpful when there is scarcity of trained staff or the patients need to be in quarantine). The device uses some of the key 4IR technologies – IoT, Artificial Intelligence, and cloud computing.

A mobile application developed for the hard of hearing. This application directly amplifies sounds and plays it over earphones, allowing differentlyabled individuals the oppurtunity to engage in conversations.

Using Machine Learning and AI to predict the success of Mergers and Acquisitions. Project is run by the FinTech Hub at Wits.

The research group (RAIL) at Wits competes in the RoboCupSoccer 3D Simulation, in which a team of 11 simulated Nau robots compete in a game of football against other teams from around the world. The focus here is both on improving the low-level control of individual robots and incorporating high-level, multi-agent decision-making into the team’s strategy.

Here, the RAIL research team focuses on learning abstract representations, which the team believes is an important component if we are ever to apply reinforcement learning to the real world. In particular, the RAIL research team focuses on skill- and symbol discovery, as well as the interplay between the two. The RAIL research team have applied their approaches to challenging pixel-based tasks that require high-level planning and has shown that symbolic representations can be learned directly from low-level sensor data.

Innovative, flexible, smart decentralized system for monitoring air and water quality. Project is sponsored by the Technology Innovation Agency (TIA), and is part of a bilaterla collaboration with with Copperbelt University, Zambia.

System supporting ealry detection of suicidal tendencies thereby enabling timely intervention and prevention of harm. The system employs a smart application to carry out non-invasive and secure monitoring of students’ internet activity to spot and flag early signs of suicidal in an automated manner.