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The von Neumann threshold of self-reproducing systems: theory and application

机译:自我复制系统的冯·诺依曼阈值:理论与应用

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This paper is devoted to the study of systems of entities that are capable of generating other entities of the same kind and, possibly, self-reproducing. The main technical issue addressed is to quantify the requirements that such entities must meet to be able to produce a progeny that is not degenerative, i.e., that has the same reproductive capability as the progenitor. A novel theory that allows an explicit quantification of these requirements is presented. The notion of generation rank of an entity is introduced, and it is proved that the generation process, in most cases, is degenerative in that it strictly and irreversibly decreases the generation rank from parent to descendent. It is also proved that there exists a threshold of rank such that this degeneracy can be avoided if and only if the entity has a generation rank that meets that threshold - this is the von Neumann rank threshold. On the basis of this threshold, an information threshold is derived, which quantifies the minimum amount of information that must be provided to specify an entity such that its descendents are not degenerative. Furthermore, a complexity threshold is obtained, which quantifies the minimum length of the description of that entity in a given language. As an application, self-assembly for a 2 Degrees of Freedom planar robot is considered, and simulation results are presented. A robot arm capable of picking up and placing the components of another arm, in the presence of errors, is considered to have successfully reproduced if these are placed within an allowable tolerance. The example shows that, due to the kinematics of the robot, errors can grow from one generation to the next, until the reproduction process fails eventually. However, error correction (via error sensing and feedback control) can then be used to prevent such degeneracy. The von Neumann generation rank and information thresholds are computed for this example, and are consistent with the simulation results in predicting degeneracy in the case without error correction, and predicting successful self-reproduction in the case with error correction.
机译:本文致力于实体系统的研究,这些系统能够生成相同类型的其他实体,并可能进行自我复制。解决的主要技术问题是量化此类实体必须具备的条件才能产生非退化的后代,即具有与祖先相同的繁殖能力。提出了一种新颖的理论,可以明确量化这些要求。引入了实体的世代等级的概念,并且证明了在大多数情况下,世代过程是退化的,因为它严格且不可逆地降低了从父代到后代的世代等级。还证明了存在等级阈值,使得只有当实体的世代等级满足该阈值时,才可以避免这种简并性-这是冯·诺依曼等级阈值。基于该阈值,导出信息阈值,该信息阈值量化为指定实体以使其子代不退化而必须提供的最小信息量。此外,获得了复杂度阈值,该阈值量化了以给定语言对该实体的描述的最小长度。作为一种应用,考虑了2自由度平面机器人的自组装,并给出了仿真结果。如果将机械手放置在允许的公差范围内,则在出现错误的情况下能够拾取并放置另一只手臂的组件的机械手被认为已成功复制。该示例显示,由于机器人的运动学,错误可能会从一代到下一代增长,直到再现过程最终失败。但是,可以使用纠错(通过错误检测和反馈控制)来防止这种退化。冯·诺依曼生成等级和信息阈值是针对此示例计算的,与模拟结果相符,在没有错误校正的情况下预测简并性,在有错误校正的情况下预测成功的自我复制。

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