Training believable agents in 3D electronic business environments using recursive-arc graphs

Using 3D Virtual Worlds for commercial activities on the Web and the development of human-like sales assistants operating in such environments are ongoing trends of E-Commerce. The majority of the existing approaches oriented towards the development of such assistants are agent-based and are focused on explicit programming of the agents' decision making apparatus. While effective in some very specific situations, these approaches often restrict agents' capabilities to adapt to the changes in the environment and learn new behaviors. In this paper we propose an implicit training method that can address the aforementioned drawbacks. In this method we formalize the virtual environment using Electronic Institutions and make the agent use these formalizations for observing a human principle and learning believable behaviors from the human. The training of the agent can be conducted implicitly using the specific data structures called recursive-arc graphs.