arXiv cs/04020572004-02cs.AI

整合可废止论证与机器学习技术

Integrating Defeasible Argumentation and Machine Learning Techniques

Sergio Alejandro Gomez, Carlos Ivan Chesñevar

备注：: 5 pages
发表信息：: Procs. WICC 2003 . Pp. 787-791. Tandil, Argentina, Mayo 2003

摘要：

机器学习（ML）领域关注的是如何构建根据经验自动改进的算法的问题。近年来，许多成功的ML应用程序已经开发，如数据挖掘程序，信息过滤系统等，虽然ML算法允许检测和提取有趣的数据模式的几种问题，这些算法中的大多数是基于定量推理，因为它们依赖于训练数据，以推断所谓的目标函数。在过去的几年中，可废止论证已被证明是一个健全的设置，以形式化常识定性推理。这种方法可以与其他推理技术相结合，例如由机器学习理论提供的那些技术。在本文中，我们概述了不同的替代方案相结合的可废止论证和机器学习技术。我们建议如何不同方面的一个通用的基于参数的框架可以与其他ML为基础的方法。

英文摘要：

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms allow the detection and extraction of interesting patterns of data for several kinds of problems, most of these algorithms are based on quantitative reasoning, as they rely on training data in order to infer so-called target functions. In the last years defeasible argumentation has proven to be a sound setting to formalize common-sense qualitative reasoning. This approach can be combined with other inference techniques, such as those provided by machine learning theory. In this paper we outline different alternatives for combining defeasible argumentation and machine learning techniques. We suggest how different aspects of a generic argument-based framework can be integrated with other ML-based approaches.

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