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Experience with a learning personal assistant

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TLDR
The design of one particular learning assistant is described: a calendar manager, called CAP (Calendar APprentice), that learns user scheduling preferences from experience and suggests that machine learning methods may play an important role in future personal software assistants.
Abstract
Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing work-flow will require significant customization to each individual user. For example, an assistant that helps schedule a particular user’s calendar will have to know that user’s scheduling preferences. This paper explores the potential of machine learning methods to automatically create and maintain such customized knowledge for personal software assistants. We describe the design of one particular learning assistant: a calendar manager, called CAP (Calendar APprentice), that learns user scheduling preferences from experience. Results are summarized from approximately five user-years of experience, during which CAP has learned an evolving set of several thousand rules that characterize the scheduling preferences of its users. Based on this experience, we suggest that machine learning methods may play an important role in future personal software assistants.

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References
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Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Journal ArticleDOI

Induction of Decision Trees

J. R. Quinlan
- 25 Mar 1986 - 
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book

Learning internal representations by error propagation

TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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Improving Retrieval Performance by Relevance Feedback

TL;DR: Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce query formulations following an initial retrieval operation to demonstrate the effectiveness of the various methods.
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How do you get the hands on experience in machine learning?

Based on this experience, we suggest that machine learning methods may play an important role in future personal software assistants.