Computer Science – Learning
Scientific paper
2005-06-22
Computer Science
Learning
8 pages, submitted to NIPS 2005
Scientific paper
We propose a new framework for building and evaluating machine learning algorithms. We argue that many real-world problems require an agent which must quickly learn to respond to demands, yet can continue to perform and respond to new training throughout its useful life. We give a framework for how such agents can be built, describe several metrics for evaluating them, and show that subtle changes in system construction can significantly affect agent performance.
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