Inductive Logic Programming - 28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018, Proceedings
Inductive Logic Programming - 28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018, Proceedings
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归纳逻辑编程 - 第 28 届国际会议，ILP 2018，意大利费拉拉，2018 年 9 月 2-4 日，会议记录
DOI：
10.1007/978-3-319-99960-9_3
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Hocquette C
中科院分区：
文献类型：
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作者：
Hocquette C
在科学中，实验是经验观察，允许对相互竞争的假设和知识获取进行仲裁。对于旨在学习代理策略的科学家来说，进行实验需要成本。从这个意义上说，学习过程的效率取决于所进行的实验的数量。我们在本文中研究了如何通过主动学习来学习有效的代理策略来降低实验成本。我们考虑元解释学习框架的扩展，该框架在假设空间上分配贝叶斯后验分布。在每次迭代中，学习器查询具有最大熵的实例的标签。这对剩余的竞争假设产生了最大的判别力，从而实现了版本空间的最大收缩。我们研究理论框架，并评估学习常规语法和代理策略任务的实验成本收益：我们的结果表明，达到任意准确度水平所需的实验数量至少可以减少一半。
In science, experiments are empirical observations allowing for the arbitration of competing hypotheses and knowledge acquisition. For a scientist that aims at learning an agent strategy, performing experiments involves costs. To that extent, the efficiency of a learning process relies on the number of experiments performed. We study in this article how the cost of experimentation can be reduced with active learning to learn efficient agent strategies. We consider an extension of the meta-interpretive learning framework that allocates a Bayesian posterior distribution over the hypothesis space. At each iteration, the learner queries the label of the instance with maximum entropy. This produces the maximal discriminative over the remaining competing hypotheses, and thus achieves the highest shrinkage of the version space. We study the theoretical framework and evaluate the gain on the cost of experimentation for the task of learning regular grammars and agent strategies: our results demonstrate the number of experiments to perform to reach an arbitrary accuracy level can at least be halved.