Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ideal Refinement of Datalog Programs
LOPSTR '95 Proceedings of the 5th International Workshop on Logic Programming Synthesis and Transformation
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Statistical Imitative Learning from Perceptual Data
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Incremental learning and concept drift in INTHELEX
Intelligent Data Analysis
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Imitation Learning Using Graphical Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
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Imitative learning can be considered an essential task of humans development. People use instructions and demonstrations provided by other human experts to acquire knowledge. In order to make an agent capable of learning through demonstrations, we propose a relational framework for learning by imitation. Demonstrations and domain specific knowledge are compactly represented by a logical language able to express complex relational processes. The agent interacts in a stochastic environment and incrementally receives demonstrations. It actively interacts with the human by deciding the next action to execute and requesting demonstration from the expert based on the current learned policy. The framework has been implemented and validated with experiments in simulated agent domains.