Socially Driven Multiagent Learning

Machine learning techniques have become ubiquitous. We find them in our smart phones/watches, appliances, when we search the web, social networks and are even becoming mainstream in showing us personalized web content. However, little is known about the implications when two or more machine learning algorithms face each other. This textbook offers a comprehensive view of the problems faced when such algorithms share the same environment and some proposed solutions to alleviate such problems.

Autorentext

Enrique Munoz de Cote is a computer scientist at the Institute of Astrophysics, Optics and Electronics, Mexico. His research connects computer science and economic theory through machine learning and game theory. He has received different awards and is a member of the board of directors of the Association for Trading Agent Research (ATAR).