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arXiv 2102.06112cs.AI
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通用人工智能从理论到实践的元模型与框架

A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

Cisco（思科公司）

Hugo Latapie, Ozkan Kilic, Gaowen Liu, Yan Yan, Ramana Kompella, Pei Wang, Kristinn R. Thorisson, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa

备注：
arXiv admin note: text overlap with arXiv:2008.12879
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摘要：

本文介绍了一种新的基于元模型的知识表示，显着提高自主学习和适应。虽然对混合机器学习/符号人工智能系统的兴趣正在日益普及，例如，推理和知识图谱，但我们发现仍然需要一个明确的知识定义和一个元模型来指导知识的创建和操作。我们在本文中介绍的元模型的一些好处包括符号接地问题的解决方案、累积学习和联邦学习。我们已经将元模型应用到从时间序列分析、计算机视觉和自然语言理解的问题中，并且发现元模型使得从机器学习到图网络分析和通过推理引擎的学习的各种各样的学习机制能够以高度协同的方式进行互操作。我们的基于元模型的项目一直表现出前所未有的准确性、性能和泛化能力。本文的灵感来自AGI的最先进的方法，最近的AGI有志的工作，粒度计算社区，以及阿尔弗雷德Korzybski的一般语义。元模型的一个令人惊讶的结果是，它不仅使机器智能的自主学习和最佳功能达到了一个新的水平，而且还可能为更好地理解如何改善人类认知提供了一条道路。

英文摘要：

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning / symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning, and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision, and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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