Training Example Generation Method for Supervised Learning Agents in Sequential Scenarios

In this paper we propose a method of training example generation from agent's experience, which is suitable for sequential sce- narios. The experience consists of the agent's observations and its action records. Examples generated are used by the agent to learn a classifier, which is used to make decisions about its strategy in the following problem instances. The method is tested in a Sovereign environment, which is an economics simulation created to test agent-based learning. Experimental results show that an agent using the proposed methods is able to learn and achieves better results than random and heuristic agents.