The study of issues arising in building agents competent in interacting with other agents has played a central role in much of AI research. Clearly, an automated intelligent agent operating in a realistic environment will often need to interact with other agents to achieve its goals. Agent modeling -- the capability of modeling and reasoning about other agent’s knowledge, beliefs, goals, and actions - is central to intelligent interaction. This capability is being addressed in a variety of research areas, including distributed AI (DAI) and multiagent systems, natural language discourse, plan recognition, human-computer interaction, intelligent tutoring and user interfaces, as well as in related areas, such as game theory, and cognitive science and psychology. A variety of techniques are being used, from building theoretical models to implementing actual systems, within all of the areas. Added impetus to this work comes from the recent explosion of work on agents in dynamic interactive (virtual reality) simulation, software (such as information retrieval), and related environments. These environments bring with them a new set of concerns for agent modeling for collaboration and competition.