Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Representing Temporal Knowledge for Case-Based Prediction
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
On the Automatic Generation of Cases Libraries by Chunking Chess Games
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
Automated case creation and management for diagnostic CBR systems
Applied Intelligence
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Case-Based Planning and Execution for Real-Time Strategy Games
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case Authoring: From Textual Reports to Knowledge-Rich Cases
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Complexity-guided case discovery for case based reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Evaluating the effectiveness of exploration and accumulated experience in automatic case elicitation
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Creation of DEVS models using imitation learning
Proceedings of the 2010 Summer Computer Simulation Conference
On combining decisions from multiple expert imitators for performance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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When learning by observing an expert, cases can be automatically generated in an inexpensive manner. However, since this is a passive method of learning the observer has no control over which problems are solved and this can result in case bases that do not contain a representative distribution of the problem space. In order to overcome this we present a method to incorporate active learning with learning by observation . Problems that are not covered by the current case base are automatically detected, during runtime or by examining secondary case bases, and presented to an expert to be solved. However, we show that these problems can not be presented to the expert individually but need to be part of a sequence of problems. Creating this sequence of cases is non-trivial, and an approach to creating such sequences is described. Experimental results, in the domain of simulated soccer, show our approach to be useful not only for increasing the problem coverage of the case base but also in creating cases with rare solutions.