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» When Random Play is Optimal Against an Adversary
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SPAA
2004
ACM
14 years 4 months ago
Fighting against two adversaries: page migration in dynamic networks
Page migration is one of the fundamental subproblems in the framework of data management in networks. It occurs in a distributed network of processors sharing one indivisible memo...
Marcin Bienkowski, Miroslaw Korzeniowski, Friedhel...
ICML
2003
IEEE
14 years 11 months ago
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Oppon
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
Vincent Conitzer, Tuomas Sandholm
PKC
1999
Springer
102views Cryptology» more  PKC 1999»
14 years 3 months ago
How to Enhance the Security of Public-Key Encryption at Minimum Cost
This paper presents a simple and efficient conversion from a semantically secure public-key encryption scheme against passive adversaries to a non-malleable (or semantically secure...
Eiichiro Fujisaki, Tatsuaki Okamoto
CONCUR
2005
Springer
14 years 4 months ago
Games Where You Can Play Optimally Without Any Memory
Abstract. Reactive systems are often modelled as two person antagonistic games where one player represents the system while his adversary represents the environment. Undoubtedly, t...
Hugo Gimbert, Wieslaw Zielonka
ATAL
2009
Springer
14 years 5 months ago
Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties
How do we build multiagent algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human intera...
James Pita, Manish Jain, Fernando Ordó&ntil...