发表机构
Aston Centre for AI Research and Application, Aston University（阿斯顿大学阿斯顿人工智能研究与应用中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过进化可塑性人工神经网络智能体在觅食环境中的实验，探究社会信息使用进化的最小环境条件，发现环境复杂性是关键因素。
AI 中文摘要
进化可塑性人工神经网络（EPANNs）由两个主要过程组成：第一个是进化，第二个是发育和生命期学习。在社会性学习起源的背景下，基于EPANN要求使用ALIFE模型进行的研究非常少。该领域的研究通常涉及模仿性的教师/学生关系。然而，这忽略了观察到的行为可能是社会信息线索的结果，而非直接模仿或教学的可能性。从EPANN过程的第一个（进化）开始，使用基于人工神经网络（ANN）的智能体在各种觅食环境中进行了一系列实验，以检验在何种最小环境条件下社会信息的使用可能进化，以达到指定适应度标准所需的代数来衡量。NEAT（增强拓扑的神经进化）被用作ANN，因为其进化算法会进化网络的拓扑结构及其权重。在实验中，无意中有一个基于最近食物位置简单网络拓扑，使智能体能够迅速达到适应度标准。有了这种拓扑，额外的信息，无论是社会性的还是其他，都不需要，并且可能成为障碍。然而，这确实表明，为了社会信息的使用能够进化，环境需要更大的复杂性。
英文摘要
Evolved Plastic Artificial Neural Networks (EPANNs) consist of two principal processes, the first, evolution, and the second, development and in-life learning. In the context of the origins of social = learning, very few studies have been carried out using ALIFE models based on EPANN requirements. Studies in this field have usually involved an imitative teacher/pupil relationship. This, however, ignores the possibility that the observed behaviour is a consequence of social information cues rather than direct imitation or teaching. Starting with the first of the EPANN processes (evolution), a series of experiments was undertaken using artificial neural network (ANN) based agents in a variety of foraging environments to examine under what minimal environmental conditions the use of social information might have evolved, as measured by the number of generations taken to meet a specified fitness criterion. NEAT (Neuroevolution of Augmenting Topologies) was the ANN used as its evolutionary algorithm would evolve a network's topology as well its weights. Unintentionally, in the experiment there was a simple network topology based on the location of the nearest food item which enabled agents to swiftly meet the fitness criterion. With this topology, additional information, social or otherwise, was not required and could have proved to be a hindrance. However, this does indicate that for the use of social information to have evolved, it would require a greater degree of complexity in the environment to do so.
