On Human-like Performance Artificial Intelligence: A Demonstration Using an Atari Game

Seng-Beng Ho, Xiwen Yang, Therese Quieta, Gangeshwar Krishnamurthy, Fiona Liausvia; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 9-12

Despite the progress made in AI, especially in the successful deployment of deep learning for many useful tasks, the systems involved typically require a huge number of training instances, and hence a long time for training. As a result, these systems are not able to rapidly adapt to changing rules and constraints in the environment. This is unlike humans, who are usually able to learn with only a handful of experiences. This hampers the deployment of, say, an adaptive robot that can learn and act rapidly in the ever-changing environment of a home, office, factory, or disaster area. Thus, it is necessary for an AI or robotic system to achieve human performance not only in terms of the "level" or "score" (e.g., success rate in classification, score in Atari game playing, etc.) but also in terms of the speed with which the level or score can be achieved. In contrast with earlier DeepMind's effort on Atari games, we describe a system that is able to learn causal rules rapidly in an Atari game environment and achieve human-like performance in terms of both score and time.