A methodology for developing self-explaining agents for virtual training

Intelligent agents are used to generate the behavior of characters in virtual training systems. To increase trainees' insight in played training sessions, agents can be equipped with capabilities to explain the reasons for their actions. By using an agent programming language in which declarative aspects of an agent's reasoning process are explicitly represented, explanations revealing the underlying motivations for agents' actions can be obtained. In this paper, a methodology for developing self-explaining agents in virtual training systems is proposed, resulting in agents that can explain their actions in terms of beliefs and goals. © 2010 Springer-Verlag