Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Rapidly adapting artificial neural networks for autonomous navigation
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
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
Using background knowledge to speed reinforcement learning in physical agents
Proceedings of the fifth international conference on Autonomous agents
Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Separating Skills from Preference: Using Learning to Program by Reward
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Behavioral Cloning of Student Pilots with Modular Neural Networks
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
Value-driven agents
Teleo-reactive programs for agent control
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
LEAP: a learning apprentice for VLSI design
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
First steps toward natural human-like HRI
Autonomous Robots
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
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This paper addresses the problem of learning control skills from observation. In particular, we show how to infer a hierarchical, reactive program that reproduces and explains the observed actions of other agents, specifically the elements that are shared across multiple individuals. We infer these programs using a three-stage process that learns flat unordered rules, combines these rules into a classification hierarchy, and finally translates this structure into a hierarchical reactive program. The resulting program is concise and easy to understand, making it possible to view program induction as a practical technique for knowledge acquisition.