发帖
雷达卡
摘要翻译：
在历史上，人类试图将日常任务传递给其他生物，这是文明兴起的主要原因。它始于部署动物来自动化农业（公牛）、运输（例如马和驴）甚至通信（鸽子）领域的任务。几千年后，随着“Al-Jazari”和其他穆斯林发明家的黄金时代到来，他们是自动化的先驱，这催生了几个世纪后的欧洲工业革命。在十九世纪末，一个新的时代开始了，计算时代，这是推动人类的最先进的技术和科学发展，也是所有科学发展背后的原因；例如医学、通讯、教育和物理。在这个技术的边缘，工程师和科学家正试图建立一个行为与他们相同的机器模型，这促使我们思考设计和实现“思考的东西”，然后是人工智能。在这篇文章中，我们将涵盖机器认知领域的每一个主要发现和研究，它们是“基本感知者和记忆者”(EPAM)和“一般问题解决者”(GPS)。第一种主要集中于实现人-语言学习行为，而第二种则试图建立一个能够解决一般问题（如定理证明、下棋和算术）的体系结构。我们将涵盖每个模型的主要目标和主要思想，以及比较它们的优缺点，最后给出它们的应用领域。最后，我们将提出一个认知机器的现实生活实现。
---
英文标题：
《Machine Cognition Models: EPAM and GPS》
---
作者：
Ali Elouafiq
---
最新提交年份：
2012
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
---
英文摘要：
Through history, the human being tried to relay its daily tasks to other creatures, which was the main reason behind the rise of civilizations. It started with deploying animals to automate tasks in the field of agriculture(bulls), transportation (e.g. horses and donkeys), and even communication (pigeons). Millenniums after, come the Golden age with "Al-jazari" and other Muslim inventors, which were the pioneers of automation, this has given birth to industrial revolution in Europe, centuries after. At the end of the nineteenth century, a new era was to begin, the computational era, the most advanced technological and scientific development that is driving the mankind and the reason behind all the evolutions of science; such as medicine, communication, education, and physics. At this edge of technology engineers and scientists are trying to model a machine that behaves the same as they do, which pushed us to think about designing and implementing "Things that-Thinks", then artificial intelligence was. In this work we will cover each of the major discoveries and studies in the field of machine cognition, which are the "Elementary Perceiver and Memorizer"(EPAM) and "The General Problem Solver"(GPS). The First one focus mainly on implementing the human-verbal learning behavior, while the second one tries to model an architecture that is able to solve problems generally (e.g. theorem proving, chess playing, and arithmetic). We will cover the major goals and the main ideas of each model, as well as comparing their strengths and weaknesses, and finally giving their fields of applications. And Finally, we will suggest a real life implementation of a cognitive machine.
---
PDF链接：
https://arxiv.org/pdf/1204.1653
京公网安备 11010802022788号
