What kinds of knowledge can Soar/IFOR agents learn in the combat simulation environment? In our investigations so far, we have found a number of learning opportunities in our systems, which yield several types of learned rules. For example, some rules speed up the agents' decision making, while other rules reorganize the agent's tactical knowledge for the purpose of on-line explanation generation. Yet, it is also important to ask a second question: Can machine learning make a significant difference in Soar/IFOR agent performance? The main issue here is that battlefield simulations are a real-world application of AI technology. The threshold which machine learning must surpass in order to be useful in this environment is therefore quite high. It is not sufficient to show that machine learning is applicable "in principle" via small-scale demonstrations; we must also demonstrate that learning provides significant benefits that outweigh any hidden costs. Thus, the overall objective of this work is to determine how machine learning can provide practical benefits to real-world applications of artificial intelligence. Our results so far have identified instances where machine learning succeeds in meeting these various requirements, and therefore can be an important resource in agent development. We have conducted extensive learning experiments in the laboratory, and have conducted demonstrations employing agents that learn; to date, however, learning has not yet been employed in large-scale exercises. The role of machine learning in Soar/IFOR is expected to broaden as practical impediments to learning are resolved, and the capabilities that agents are expected to exhibit are broadened.