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In this paper we investigate the approximation properties of the coarse-graining procedure applied to kinetic Monte Carlo simulations of lattice stochastic dynamics. We provide both analytical and num...
In this paper we investigate the approximation properties of the coarse-graining procedure applied to kinetic Monte Carlo simulations of lattice stochastic dynamics. We provide both analytical and num...
Using laboratory experimental data, we test the uncertainty of social state transitions in various competing environments of fixed paired two-person constantsum 2×2 games. It firstly shows that,the di...
Approachability has become a standard tool in analyzing learning algorithms in the adversarial online learning setup. We develop a variant of approachability for games where there is ambiguity in th...
Minimax Policies for Combinatorial Prediction Games     Minimax Policies  Combinatorial  Prediction  Games       font style='font-size:12px;'> 2011/6/20
We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors. Our goal is to understand the magnitude of the minimax regret for the worst pos...
Partial-monitoring games constitute a mathematical framework for sequential decision making problems with imperfect feedback: The learner repeatedly chooses an action, opponent responds with an outcom...
On zero - sum stochastic games with general state space. I。
Stopping games for symmetric Markov processes      Stopping games  symmetric Markov processes        font style='font-size:12px;'> 2009/9/24
Stopping games for symmetric Markov processes
On zero-sum stochastic games with general state space. II。

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