PROWLER.io’s platform has many potential uses but is initially focusing on game development, autonomous vehicles (AVs), drones, robotics and smart cities.
In game development, PROWLER.io’s platform replaces the use of hand-crafted rules, which are time consuming, expensive and restrictive for decision making. This produces games that feel open and responsive and engage players in novel, more personalised ways. In addition, PROWLER.io’s agents can be designed to perform repetitive tasks thousands of times faster than manual testers, thus significantly reducing game development costs and time to market.
It is impossible to program AVs for every eventuality they will face on the roads. PROWLER.io’s technology uses probabilistic modelling to enable a self-learning car to “understand” itself and its environment. Multiple principled learning approaches are used to teach it to drive, together with multi-agent systems to ensure that it operates safely alongside other road users.
In smart cities, the platform optimises fleet planning and management. This ensures that real time demand for AVs matches supply, vehicles are close by when needed, routes are planned efficiently, congestion is reduced and negative environmental impacts are minimised.
Andrew Williamson, Investment Director at CIC, who is joining the Board of PROWLER.io commented, “PROWLER.io has assembled a world-class team of researchers to tackle some of the most intractable problems of our age. It is hugely exciting that the company is able to capitalise on the expertise in probabilistic modelling, principled machine learning and game theory available in Cambridge. The combination of PROWLER.io’s team and technology applied to the important problems they are solving provides a significant commercial opportunity for the company.”
Vishal Chatrath, CEO of PROWLER.io added, “This investment allows us to expand our world-leading team of academics and developers, enhancing our research bandwidth and accelerating our technology into the market. As a team, we will use the funding to take the business to the next stage and we will continue to solve some of the world’s hardest machine learning problems.”