The ability to build intelligent agents is significantly constrained by the knowledge acquisition effort required. Many iterations by human experts and knowledge engineers are currently necessary to develop knowledge-based agents with acceptable performance. We have developed a novel approach, called Disciple, for building intelligent agents that relies on an interactive tutoring paradigm, rather than the traditional knowledge engineering paradigm. In the Disciple approach, an expert teaches an agent through five basic types of interactions. Such rich interaction is rare among machine learning systems, but is necessary to develop more powerful systems. These interactions, from the point of view of the expert, include: specifying knowledge to the agent; giving the agent a concrete problem and its solution that the agent is to learn a general rule for; validating analogical problems and solutions proposed by the agent; explaining to the agent reasons for the validation; and being guided to provide new knowledge during interaction. In this paper we illustrate these basic learning interactions between an expert and an intelligent agent in the context of teaching the agent for military training simulations.