Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in multiagent systems
Multiagent systems
Machine Learning
Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Combining Multiple Perspectives
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Empirical evaluation of ad hoc teamwork in the pursuit domain
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
An analysis framework for ad hoc teamwork tasks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Teaching on a budget: agents advising agents in reinforcement learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Knowledge transfer between expert and novice agents is a challenging problem given that the knowledge representation and learning algorithms used by the novice learner can be fundamentally different from and inaccessible to the expert trainer. We are particularly interested in team tasks, robotic or otherwise, where new teammates need to replace currently indisposed team member(s). We are interested in a general knowledge transfer framework where existing team members or experts can train a new agent to follow its role in team coordination by using exemplars of desirable behavior. Each such exemplar presents a team situation and a preferred action. We envisage an iterative training process where the trainer selects more exemplars in the next iteration based on the errors made by the learner in action choices for test exemplars presented in the current iteration. Such an iterative, exemplar based generic knowledge transfer scheme can be used by agents using arbitrary knowledge representation and learning methods. We evaluate the success of training new teammates in the well-known pursuit problem, where some of the current set of expert predators is being replaced by new ones with no a priori hunting knowledge. Experimental results demonstrate the robustness of our knowledge transfer scheme with a graceful performance degradation.