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arXiv cs/0404032cs.LGcs.AIcs.NE
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什么时候差异很重要？基于认知经济的在线特征提取

When Do Differences Matter? On-Line Feature Extraction Through Cognitive Economy

David J. Finton

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20 pages, 10 PostScript figures, LaTeX2e
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摘要：

对于一个真正自主的智能代理来说，它必须能够在与世界交互时使其表示适应其任务的要求。目前大多数在线特征提取的方法是特设的，相比之下，本文提出了一种算法，根据判断的状态兼容性和状态空间抽象的原则性标准来自认知经济的心理学原理。该算法采用主动形式的Q学习，并通过合并和分裂Voronoi区域来划分连续状态空间。实验说明了一种新的方法来测试和比较表示的学习曲线。从puck-on-a-hill任务的结果表明，该算法的能力，学习有效的表示，优于那些产生的其他一些，众所周知的方法。

英文摘要：

For an intelligent agent to be truly autonomous, it must be able to adapt its representation to the requirements of its task as it interacts with the world. Most current approaches to on-line feature extraction are ad hoc; in contrast, this paper presents an algorithm that bases judgments of state compatibility and state-space abstraction on principled criteria derived from the psychological principle of cognitive economy. The algorithm incorporates an active form of Q-learning, and partitions continuous state-spaces by merging and splitting Voronoi regions. The experiments illustrate a new methodology for testing and comparing representations by means of learning curves. Results from the puck-on-a-hill task demonstrate the algorithm's ability to learn effective representations, superior to those produced by some other, well-known, methods.

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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