Alan Turing proposed in 1950 a framework called an imitation game to decide
if a machine could think. Using mathematics developed largely after Turing --
category theory -- we analyze a broader class of universal imitation games
(UIGs), which includes static, dynamic, and evolutionary games. In static
games, the participants are in a steady state. In dynamic UIGs, "learner"
participants are trying to imitate "teacher" participants over the long run. In
evolutionary UIGs, the participants are competing against each other in an
evolutionary game, and participants can go extinct and be replaced by others
with higher fitness. We use the framework of category theory -- in particular,
two influential results by Yoneda -- to characterize each type of imitation
game. Universal properties in categories are defined by initial and final
objects. We characterize dynamic UIGs where participants are learning by
inductive inference as initial algebras over well-founded sets, and contrast
them with participants learning by conductive inference over the final
coalgebra of non-well-founded sets. We briefly discuss the extension of our
categorical framework for UIGs to imitation games on quantum computers.

图灵提出了 1950 年模仿游戏的框架，通过在范畴理论中使用数学，我们分析了更广泛的通用模仿游戏（UIGs），包括静态、动态和进化游戏，并使用两个有影响力的 Yoneda 结果来描述每种类型的模仿游戏。

通用模仿游戏

Universal Imitation Games

Text editing, such as grammatical error correction, arises naturally from
imperfect textual data. Recent works frame text editing as a multi-round
sequence tagging task, where operations -- such as insertion and substitution
-- are represented as a sequence of tags. While achieving good results, this
encoding is limited in flexibility as all actions are bound to token-level
tags. In this work, we reformulate text editing as an imitation game using
behavioral cloning. Specifically, we convert conventional sequence-to-sequence
data into state-to-action demonstrations, where the action space can be as
flexible as needed. Instead of generating the actions one at a time, we
introduce a dual decoders structure to parallel the decoding while retaining
the dependencies between action tokens, coupled with trajectory augmentation to
alleviate the distribution shift that imitation learning often suffers. In
experiments on a suite of Arithmetic Equation benchmarks, our model
consistently outperforms the autoregressive baselines in terms of performance,
efficiency, and robustness. We hope our findings will shed light on future
studies in reinforcement learning applying sequence-level action generation to
natural language processing.

本研究将文本编辑作为一项模仿游戏，使用行为克隆方法将传统序列转序列数据转化为状态 - 操作演示，通过引入双解码器结构来并行解码操作，实现对操作之间的依赖进行保留，以及利用轨迹增强技术缓解模仿学习常常遇到的分布偏移问题。对一组算术方程基准测试进行了实验，结果显示该模型在性能、效率和稳健性方面均优于自回归基线。期望该研究成果能为以后在强化学习中应用序列级操作生成的自然语言处理研究提供启示。

文本编辑作为模拟游戏

Text Editing as Imitation Game

We propose an alternative to the Turing test that removes the inherent
asymmetry between humans and machines in Turing's original imitation game. In
this new test, both humans and machines judge each other. We argue that this
makes the test more robust against simple deceptions. We also propose a small
number of refinements to improve further the test. These refinements could be
applied also to Turing's original imitation game.

我们提出了一种替代图灵测试的方法，通过使双方的人类和机器都参与评判从而消除原始模仿游戏中人与机器之间的固有不对称性，并且建议了一些改进来提高这种测试的鲁棒性。