Sandip Sen and Parijat Prosun Kar
Though various interesting research problems have been studied in the context of learning agents, few researchers have addressed the problems of one, knowledgeable, agent teaching another agent. Agents can do more than share training data, problem traces, learned policies. In particular, we investigate how an agent can use its learned knowledge to train another agent with a possibly different internal knowledge representation. We have developed an algorithm that can be used by a concept learning agent, the trainer, to iteratively select training examples for another agent, the trainee, without any assumptions about its internai concept representation. We present initial results where the trainer agent is an instance based concept learner and the trainee agent is a decision tree learner.