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.

翻译：我们考虑经典“100囚犯问题”及其变体（涉及空盒子）的推广形式，其中团队的获胜概率取决于尝试次数以及获胜者数量。我们将此称为无约束的100囚犯问题。在介绍三类主要策略后，我们定义了一系列“混合”策略并量化其获胜效率。当解析结果不可用时，我们利用蒙特卡洛模拟高精度估计获胜概率。基于所得结果，我们推测：除了最大化经典（约束）问题获胜概率的策略外，所有策略在玩家数量或空盒子数量的弱条件下均收敛于随机策略。最后，我们评论了本研究成果在理解信息检索过程（如生物体中的“记忆”）中的潜在应用。