New intelligent system learns from simple problems to solve complex ones

Researchers at The Australian National University (ANU) have designed a new type of intelligent system based on deep learning that can learn to solve decision-making problems, including problems more complex than it has been trained to solve. Deep learning is a popular artificial intelligence technique for tasks such as creating captions to describe images, transcribing speech to text and learning how to play video or board games from images alone. Lead researcher Sam Toyer said the system, called Action Schema Networks (ASNets), could hypothetically enable a robot to learn to navigate a floor with 10 rooms, and then be able to roam on a floor with thousands of rooms. "ASNets' ability to solve much larger problems is a game changer," said Mr Toyer, who developed ASNets as part of his thesis during his Bachelor of Advanced Computing (Research & Development) at ANU and was awarded a University Medal. "Using our ASNet-based system, we could potentially create new cyber-security applications that find system vulnerabilities, or design new robotics software to perform specialised tasks in automated warehouses or unmanned space missions." Mr Toyer said intelligent systems relied on automated planning technology to make decisions. "Whether it's a Mars rover choosing where to take photos, or a smart grid deciding how to isolate a fault, you need a planning algorithm to choose the best course of action." Mr Toyer said some deep learning-based systems, including AlphaGo, had been used to solve decision-making problems.