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Prof. Aumann's Lab

Prof. Aumann's Lab

 

 

Tel: 972-3-531-8629
Email: aumann@cs.biu.ac.il

 

Prof. Yonatan Aumann is a Professor in the Department of Computer Science. Aumann and his team conduct research that spans many subjects within the general field of the theory of computing, including parallel and distributed algorithms, cryptography, approximation algorithms, pattern matching, parametrized complexity and more.

In addition, Aumann and team consider applications of the theory to more practical domains, including artificial intelligence, social choice and interaction, and computational biology.

Artificial Intelligence and Social Choice

Together with multi-agent and Artificial Intelligence (AI) experts Sarit Kraus and David Sarne, Aumann develops theories governing the interactions between agents, focusing on analysis of adversarial and cooperative behavior on agents, as well as on designing strategies for promoting effective inter-agent cooperation.

In this line, Aumann and team consider computational aspects of voting systems. Devising efficient and socially beneficial voting systems has been an area of active research in the social sciences, economics, and the political sciences for over a century. Aumann and team apply computer-science tools to analyze such systems, providing insights on how computational complexity can affect the behavior of participants in such systems.

In another line of research, Aumann and team consider the topic of Fair Division. Devising methods for fair division of goods among different players has been an active area of research for decades. Several fairness criteria are considered, including proportionality, envy-freeness and equitability. Here too, Aumann and his team apply techniques borrowed from theoretical computer science to analyze the problems at hand. In particular, they consider the tradeoffs between fairness and social welfare, and the computational complexity of optimizing the social welfare.

Aumann and his group also study questions regarding agent-search in physical landscapes. For example, when a robot, or multiple robots, are tasked at finding a certain good located somewhere within a physical domain, what is the best search strategy? Aumann and his team analyze this question in multiple settings, and provide efficient algorithms and techniques for conducting such a search and coordinating between the different agents.

Last updated on 1/6/14