Learning to Reason: The Non-Monotonic Case
Learning to Reason: The Non-Monotonic Case
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学习推理：非单调案例
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
D. Roth
中科院分区：
文献类型：
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作者：
D. Roth
我们提出了一种新的方法来研究人类常识推理的非单调性。这项工作的两个主要前提是，常识推理是一种归纳现象，以及在代理与环境的交互中缺失的信息可能会像观察到的信息一样为未来的交互提供信息。这种直觉是规范化的，从不完全信息的推理问题提出了一个问题的学习属性函数在一个广义域。
我们考虑的例子，说明了各个方面的非单调推理现象，多年来已被用于各种形式主义的基准，并将其转化为学习的原因问题。我们证明，这些具有简洁的表示在广义域，并证明这些表示可以有效地学习。
开发的框架提出了一个可操作的方法来研究推理，但严格的，经得起分析。我们表明，这种方法有效地支持推理与不完整的信息，并在同一时间符合我们的期望，合理的推理模式的情况下，其他理论不。
这项工作继续以前的作品在学习的原因框架和支持的论文，为了开发一个计算帐户的常识推理应该一起研究学习和推理的现象。
We suggest a new approach for the study of the non monotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon and that missing information in the interaction of the agent with the environment may be as informative for future interactions as observed information. This intuition is normalized and the problem of reasoning from incomplete information is presented as a problem of learning attribute functions over a generalized domain.
We consider examples that illustrate various aspects of the non monotonic reasoning phenomena which have been used over the years as bench marks for various formalisms and translate them into Learning to Reason problems. We demonstrate that these have concise representations over the generalized domain and prove that these representations can be learned efficiently.
The framework developed suggests an operational approach to studying reasoning that is nevertheless rigorous and amenable to analysis. We show that this approach efficiently supports reasoning with incomplete information and at the same lime matches our expectations of plausible patterns of reasoning in cases where other theories do not.
This work continues previous works in the Learning to Reason framework and supports the thesis that in order to develop a computational account for commonsense reasoning one should study the phenomena of learning and reasoning together.