This project tackles a previously unexplored problem in the relationship between human and machine learning. Many problems that challenge human intelligence (chess, Go) have yielded to modern computer algorithms. Yet some tasks that are easy for humans, or even animals — such as flexible locomotion and rapid and robust visual understanding of the surroundings are still at the cutting edge of artificial-intelligence research. Computers calculate without error. Yet, for example, quite a few people who know the difference between odd and even will say that 798 is odd, perhaps because two thirds of its digits are odd. Are there fundamental differences between the way computers learn and way humans learn? Can they be found with a rigorous study of games where the player must learn a rule by trial and error? This project uses games involving the learning of rules to explore similarities and differences between human and machine learning. It will seek new insights into human learning and may improve understanding of machine learning as well. Long-term, it aims to better integrate algorithms and humans for solving real-world problems; humans and computers work together best when they can complement each other, This project will seek generalizable distinctions between rules that are easy for humans and rules that are hard for humans; the special focus is to find problems where the order of difficulty is exactly reversed for machines. Finding the principles behind these reversals will help to triage problems. The long-term goal is hybrid systems, human and machine learning integrated to achieve goals such as medical diagnosis, treatment planning, etc. This project if successful will contribute to rigorously defining how and why some learning problems that seem relatively easier for humans are nonetheless more difficult for machines, and vice versa. With a focus on the specific activity of rule finding, this research may even shed new light on the scientific process, which has been characterized as “discovering the rules of nature.â€
This project explores complementarity between Machine Learning and Human Learning with a rigorously balanced approach, using a “rule induction†challenge that is presented to both humans and computers. Computers will use state-of-the art deep neural networks, and explore the hypothesis space of rules describable in the project’s coding language. The psychological research investigates crucial problems such as transfer learning across rules, and the role of language and naming in rule discovery. Both human and machine “players†learn the rules by trial and error. The rule encoding language, reinforcement-learning processes, and scoring systems ensure symmetry of human and machine learners. Performance measures will include discounted reward and convergence to error-free play. Learning curves will be used to measure the difficulty of learning each rule. Experimental conditions will be systematically varied, including not only the rule to be learned, but also parameters such as the minimum and maximum number of different shapes displayed, the maximum number of “boards†that a user may use in attempting to learn a given rule, and the incentive/reward structure by which players earn rewards for their performance. The research will seek identify pairs of classes of rules such that the class that is easier for humans is more difficult for computers, and vice versa. The project will involve extensive experiments using diverse machine-learning approaches, as well as Amazon Mechanical Turk for data on human learning performance."Comparing the learnability of different rules sheds new light on human learning biases, may prove useful for structuring curricula, and may help identify which gaps in knowledge are most detrimental to human problem solving. The goal is to interpret or explain what distinguishes these anomalous pairs of rule classes from others where the relative degree of difficulty is the same for humans and computers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
