Modeling Human Actions through Context

This paper presents and discusses how context is being used to model intelligent human activity – specifically, tactical actions. Tactical behavior involves selection and execution of courses of action that address the current needs of the agent. The discussion centers about the work done in the author’s research laboratory that addresses tactical behavior by an agent. A limited discussion about the works of others is also included. Two points of view are discussed vis-à-vis the tactical behavior of an agent: a) from the point of view of a performer of an action (the doer), and b) from the standpoint of one who directs others (agents or humans) to perform the actions (a manager, commander or coach). Additionally, the role that can be played by context in machine learning of tactical behavior is also discussed. This particularly focuses on learning from observation of human performance, known as LfO. LfO has been found to be an effective way for learning agents to learn how to perform certain tasks that are performed by a human and whose actions are observed (i.e., recorded in a time-stamped trace of what actions were taken when).