论文标题
根据部分信息检索：无约束的100名囚犯问题
On partial information retrieval: the unconstrained 100 prisoner problem
论文作者
论文摘要
我们考虑对古典100囚犯问题及其变体的概括，涉及空盒子，从而赢得了团队的概率取决于尝试的数量以及获奖者的数量。我们将其称为无约束的100名囚犯问题。在介绍了三个主要类策略之后，我们定义了各种“混合”策略并量化其获胜效率。每当没有分析结果时，我们就会使用蒙特卡洛模拟以高准确的胜利概率估算。根据获得的结果，我们猜测所有策略均除了最大化经典（受约束）问题的获胜概率的策略外，在弱条件下在玩家数量或空盒子的弱条件下收敛到随机策略。我们通过评论我们在理解信息检索过程中的结果的可能应用，例如在生物体中的``记忆''。
We consider a generalization of the classical 100 Prisoner problem and its variant, involving empty boxes, whereby winning probabilities for a team depend on the number of attempts, as well as on the number of winners. We call this the unconstrained 100 prisoner problem. After introducing the 3 main classes of strategies, we define a variety of `hybrid' strategies and quantify their winning-efficiency. Whenever analytic results are not available, we make use of Monte Carlo simulations to estimate with high accuracy the winning-probabilities. Based on the results obtained, we conjecture that all strategies, except for the strategy maximizing the winning probability of the classical (constrained) problem, converge to the random strategy under weak conditions on the number of players or empty boxes. We conclude by commenting on the possible applications of our results in understanding processes of information retrieval, such as ``memory'' in living organisms.
