Learning Mental Models
Learning Mental Models
复制标题
学习心理模型
作者：
Astro Teller
学习的过程并不总是像将输入映射到最佳输出那样简单。通常需要内部状态来区分世界上可观察到的相同状态。遗传编程集中于解决功能/反应竞技场中的问题，部分原因是缺乏将记忆纳入范式的自然方式。本文提出了一个简单的遗传编程范式，无缝地结合了有效的收集，存储和检索任意复杂的状态信息的演变。实验结果表明，有效的生产和使用复杂的状态结构可以演变和代理演变的内存使用快速和永久取代纯粹的反应性和非确定性功能。这些结果可能不仅有助于未来对心理模型的原因和组成的研究，而且可能扩大可以通过遗传编程实际解决的问题类型。
The process of learning is not always as simple as mapping inputs to the best outputs. Often internal state is needed to distinguish between observably identical states of the world. Genetic programming has concentrated on solving problems in the functional/reactive arena, in part because of the absence of a natural way to incorporate memory into the paradigm. This paper presents a simple addition to the genetic programming paradigm that seamlessly incorporates the evolution of the effective gathering, storage, and retrieval of arbitrarily complicated state information. Experimental results show that the effective production and use of complex state structures can be evolved and that agents evolving the use of memory quickly and permanently displace purely reactive and non-deterministic functions. These results may not only aid future research into the causes and constituents of mental models but may expand the types of problems that can be practically tackled by genetic programming.