[论文解读] Integrated information increases with fitness in the simulated evolution of autonomous agents

Jeffrey A. Edlund, Nicolas Chaumont+4 more|arXiv (Cornell University)|Mar 9, 2011

Evolutionary Game Theory and Cooperation被引用 4

一句话总结

本研究探讨了在导航任务中，模拟自主代理（animats）的信息整合与处理如何随适应度的提升而演化。随着animats在数千代中适应，信息整合与处理能力同步提升，表明信息整合是功能复杂性的有力预测指标，尤其在依赖记忆的任务中。

ABSTRACT

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent (animat) evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its fit to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.

研究动机与目标

• 使用信息论度量方法量化进化自主代理的功能复杂性。
• 研究信息整合与处理是否与模拟代理的适应度相关。
• 确定信息整合是否在记忆密集型任务中优于传统处理度量，成为适应度的更好预测指标。
• 评估这些度量是否可在无直接适应度数据的情况下，作为功能复杂性的代理指标。

提出的方法

• 在受控导航环境中对animats进行数千代的模拟进化。
• 应用多种候选度量方法，量化animat神经系统中的信息整合与处理。
• 基于任务表现（如导航成功率）对适应度进行评估，跨代进行比较。
• 将信息整合与处理度量与传统信息论度量进行对比。
• 分析进化轨迹，评估信息度量相对于适应度提升的变化。
• 使用整合信息（Φ）及相关度量作为animat认知架构中功能复杂性的代理指标。

实验结果

研究问题

• RQ1 在自主代理的模拟进化过程中，信息整合如何随适应度演化？
• RQ2 信息处理与整合度量在多大程度上能预测animats的高适应度？
• RQ3 在需要记忆的任务中，信息整合是否比传统处理度量更能预测适应度？
• RQ4 在缺乏适应度数据的情况下，信息整合能否作为功能复杂性的可靠代理？

主要发现

• 随着animats在数千代中进化，信息整合与处理能力均与适应度同步提升。
• 在需要记忆的任务中，适应度与信息整合的相关性更强，表明整合是此类任务中更优的功能复杂性度量。
• 信息处理度量也与适应度相关，但在依赖记忆的环境中，信息整合表现出更强的预测能力。
• 适应度与信息整合之间的强相关性表明，此类度量反映了潜在的功能复杂性。
• 即使缺乏适应度数据，信息整合度量仍可作为进化代理中功能复杂性的可靠指标。
• 结果支持将信息整合用作人工系统与生物系统中功能复杂性的可量化代理指标。