Collaborative Research: Mutual Learning: A Systems Theoretic Investigation

Yale University, New Haven CT

Investigators

Abstract

Mutual learning can happen between two humans, a human and machine, or between two machines. The first class is of interest to researchers in the field of social psychology. The importance of human machine interactions is being felt in many situations and most recently in the interaction between the human driven and completely autonomous vehicles. The proposed research deals with machine-machine learning to investigate efficient cooperation between machines, but also reveal the limitations of this cooperation. In particular, the research will attempt to answer questions such as whether two agents, although individually using schemes that will result in the desired behavior, may arrive at wrong conclusion using mutual learning. While the term mutual learning has been used by other investigators in the past, our objective is to investigate it in a quantitative sense within the framework of mathematical systems theory. The problems proposed for investigation include deterministic optimization in high dimensional spaces, stochastic reinforcement learning in static/stationary environments (learning automata) using both deterministic and stochastic schemes, learning in dynamic environments such as the ones described by Markov Decision Processes, and learning/adaptation by multiple agents in dynamic environments described by deterministic or stochastic difference and differential equations. The results on mutual learning obtained during the proposed project will be widely disseminated at national and international conferences as well as the bi-annual Yale Workshops on Adaptive and Learning Systems. 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.