在（噪声）迭代囚徒困境中平衡合作与适应
Balancing Cooperativeness and Adaptiveness in the (Noisy) Iterated Prisoner's Dilemma
摘要：
自从阿克塞尔罗德的开创性工作以来，锦标赛一直是迭代囚徒困境（IPD）中评估策略的主要基准。在这项工作中，我们首先介绍了一种策略的IPD优于以前的锦标赛冠军时，对239战略的阿克塞尔罗德图书馆，在IPD的噪声水平从0%到10%不等。我们的策略背后的基本思想是开始玩一种针锋相对的版本，如果他们的速度没有明显高于噪音水平，那么就原谅无缘无故的背叛，同时建立一个对手的（记忆-1）模型;然后切换到一个最适合对手模型的策略。然后，我们认为，上述策略（像其他突出的策略）缺乏一些理想的属性，这些属性没有得到锦标赛的很好测试，但在其他情况下将是相关的：我们希望我们的策略是自我合作的，即，即使在高噪音水平下，也有很高的概率与克隆人合作;我们希望它是合作诱导的，即，最佳的对抗策略应该是高概率的合作。我们表明，我们可以保证这些属性，在适度的代价，在比赛的表现，从适应对手的战略恢复到宽容的针锋相对的战略，在适当的条件下
英文摘要：
Ever since Axelrod's seminal work, tournaments served as the main benchmark for evaluating strategies in the Iterated Prisoner's Dilemma (IPD). In this work, we first introduce a strategy for the IPD which outperforms previous tournament champions when evaluated against the 239 strategies in the Axelrod library, at noise levels in the IPD ranging from 0% to 10%. The basic idea behind our strategy is to start playing a version of tit-for-tat which forgives unprovoked defections if their rate is not significantly above the noise level, while building a (memory-1) model of the opponent; then switch to a strategy which is optimally adapted to the model of the opponent. We then argue that the above strategy (like other prominent strategies) lacks a couple of desirable properties which are not well tested for by tournaments, but which will be relevant in other contexts: we want our strategy to be self-cooperating, i.e., cooperate with a clone with high probability, even at high noise levels; and we want it to be cooperation-inducing, i.e., optimal play against it should entail cooperating with high probability. We show that we can guarantee these properties, at a modest cost in tournament performance, by reverting from the strategy adapted to the opponent to the forgiving tit-for-tat strategy under suitable conditions
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