Athlone Institute of Technology, Athlone, Ireland.
Artif Life. 2012 Fall;18(4):399-423. doi: 10.1162/ARTL_a_00074. Epub 2012 Aug 31.
We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.
我们描述了一个研究项目的初始阶段，该项目旨在开发一个人工智能框架，以最小的准备或启动时间从大数据集中提取知识。在这个阶段，我们使用一种新的大脑架构来进化人工智能种群。这些代理具有足够的智能，可以发现数据中的模式，并根据这些模式做出生存决策。该物种使用二倍体繁殖、海布学习和科恩恩自组织映射，结合使用模式丰富的数据作为环境和将数据分析作为人工智能生存问题等新颖技术。第一代代理很好地掌握了模式发现任务，从而得以茁壮成长。进化通过使代理更加悲观，同时使他们的大脑更有效率，进一步适应了他们的环境。
