Integrating Quantitative and Qualitative Discovery: The ABACUS System
Integrating Quantitative and Qualitative Discovery: The ABACUS System
复制标题
整合定量和定性发现：ABACUS 系统
DOI：
10.1023/a:1022866732136
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发表时间：
1990
期刊：
影响因子：
7.5
通讯作者：
R. Michalski
中科院分区：
文献类型：
--
作者：
Brian Falkenhainer;R. Michalski
大多数关于归纳学习的研究关注的是定性学习，即从给定的事实中归纳出概念性的、逻辑式的描述。相比之下，定量学习处理的是发现表征经验数据的数值规律。本研究试图将这两种类型的学习结合起来，将新发展的启发式公式化方法与之前发展的概念学习方法结合在归纳学习程序AQ11中。由此产生的系统ABACUS，制定了绑定观测数据子集的方程，并派生出明确的、逻辑风格的描述，说明这些方程的适用性条件。此外，还介绍了几种新的定量学习技术。单位分析通过考察变量单位的相容性，减少了方程的搜索空间。比例图搜索解决了识别应该进入方程的相关变量的问题。悬架搜索通过启发式评估来集中搜索空间。通过物理和化学的几个例子，说明了ABACUS的功能。
Most research on inductive learning has been concerned with qualitative learning that induces conceptual, logic-style descriptions from the given facts. In contrast, quantitative learning deals with discovering numerical laws characterizing empirical data. This research attempts to integrate both types of learning by combining newly developed heuristics for formulating equations with the previously developed concept learning method embodied in the inductive learning program AQ11. The resulting system, ABACUS, formulates equations that bind subsets of observed data, and derives explicit, logic-style descriptions stating the applicability conditions for these equations. In addition, several new techniques for quantitative learning are introduced. Units analysis reduces the search space of equations by examining the compatibility of variables' units. Proportionality graph search addresses the problem of identifying relevant variables that should enter equations. Suspension search focusses the search space through heuristic evaluation. The capabilities of ABACUS are demonstrated by several examples from physics and chemistry.