Moshe Tennenholtz

Yoav Shoham and Moshe Tennenholtz have separately and jointly made significant contributions at the intersection of computer science, game theory, and economics, most particularly in multi-agent systems, social coordination, and related domains. Both are widely cited for seminal work in these areas. Yoav's pioneering work in agent-oriented programming provided a methodology for specifying distributed multi-agent systems, and his work in combinatorial auctions and mechanism design is equally foundational. Moshe created RMax  one of the best known reinforcement learning algorithms, and introduced the concept of program equilibrium to the analysis of Internet economies.

As collaborators, they have provided foundational contributions to the areas of social coordination, preceding the recent growth of activity in social networks. Their work established a framework for addressing coordination of multiple agents in distributed settings, and established a basis for examining a wide range of important questions and for developing influential applications in electronic commerce, combinatorial auctions, social computing, equilibrium computation, mechanism design, and multi-agent learning. Each has individually won the ACM SIGART Autonomous Agents Research Award for his own contributions to autonomous agents and multiagent systems.

Yoav and Moshe have also been influential in other areas. Yoavs textbook on multi-agent systems (with Kevin Leyton-Brown) is widely praised by key researchers from AI, operations research, philosophy, political science, economics and game theory, biology, and statistics. Moshe is acknowledged as a central contributor to many of Microsofts pricing algorithms for online advertising. Both have served as program or general chairs for key conferences and on editorial boards of numerous journals. And both have trained impressive cohorts of students.