C4.5: programs for machine learning
C4.5: programs for machine learning
Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Agents that reduce work and information overload
Communications of the ACM
Experience with a learning personal assistant
Communications of the ACM
Rapid identification of repeated patterns in strings, trees and arrays
STOC '72 Proceedings of the fourth annual ACM symposium on Theory of computing
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Programming by Demonstration Using Version Space Algebra
Machine Learning
Comparing end-user and intelligent remote control interface generation
Personal and Ubiquitous Computing
EMMA: modèle utilisateur pour la plasticité des interfaces homme-machine en mobilité
UbiMob '08 Proceedings of the 4th French-speaking conference on Mobility and ubiquity computing
Supporting co-evolution of users and systems by the recognition of interaction patterns
Proceedings of the Working Conference on Advanced Visual Interfaces
Using ensembles of decision trees to automate repetitive tasks in web applications
Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems
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The APE (Adaptive Programming Environment) project focuses on applying Machine Learning techniques to embed a software assistant into the VisualWorks Smalltalk interactive programming environment. The assistant is able to learn user's habits and to automatically suggest to perform repetitive tasks on his behalf. This paper describes our assistant and focuses more particularly on the learning issue. It explains why state-of-the-art Machine Learning algorithms fail to provide an efficient solution for learning user's habits, and shows, through experiments on real data that a new algorithm we have designed for this learning task, achieves better results than related algorithms.