Conceitos essenciais
Hostile players aim to conceal their identity by behaving like non-hostile players, leading to equilibrium policies for identity concealment games.
Resumo
Identity concealment games involve hostile players concealing their identity from opponents. The average player's policy is crucial for measuring identity concealment. Equilibrium policies are essential for optimal gameplay strategies. The KL divergence objective function plays a key role in understanding the dynamics of deception in game theory. The goal is to learn near-optimal policies while maintaining anonymity.
Players must navigate adversarial environments while avoiding exposure and achieving strategic objectives. Deception and game balancing are critical aspects of zero-sum games. Learning algorithms play a vital role in optimizing gameplay strategies.
Estatísticas
P∞ t=0 Prπ1,inf ,π2(st = s) = ∞ for some s ∈ S+\SR and π1,inf ∈ Π1, then C(π1,inf, π2) = ∞.
For every s ∈ S+, we have KL(π1(s)||πAv(s)) < ∞ since π1,fin takes only permissible actions.
If PrπAv,π2(♦ ⃝ {st}|ht) = 1 for some π2 ∈ Π2,St, then we have PrπAv,π2(♦SR|st) = 0 and st must be a trap state.
Let N Av,2s be a random variable denoting the number of times that s ∈ S+ \ SR appears in a random run under πAv and π2.
Let N 1,2s be a random variable denoting the number of times that s ∈ S+ \ SR appears in a random run under π1 and π2.
Citações
"Hostile players aim to make their win look coincidental while concealing their true intentions."
"Equilibrium policies are crucial for achieving optimal strategies in identity concealment games."
"The KL divergence objective function provides insights into the dynamics of deception in game theory."