On Theoretical Questions of Machine Learning, Multi-Agent Systems, and Quantum Computing with Their Reciprocal Applications

Abstract

Recent advances in Multi-Agent Systems (MAS) have shown the importance of this field in computer science. Applications can vary in many different research areas in which the problems can be tackled with distributional AI, like economics, sociology, and psychology. However, there are still challenges and open questions to be answered. Cooperation among agents, implies the existence of a complex connection. Connections can be analysed using GNNs. On the other hand, an agent, per se, should be flexible and adapted to the environment which can be done using RL. In this proposal we are mentioning some challenges and open questions that can be raised by combining these methods in MAS. Additionally, quantum computing is introduced that can fasten the computational effort of ML and MAS programs.