This thesis investigates an intelligent system that can in real time infer the course of action of a human opponent in a competitive environment. Such an achievement would indicate the possibility that machines can not only interpret human behavior as it happens, but also predict the future course of action that a human might take. This thesis first examines several different application of intention recognition, describes the approach of Template Based Interpretation (TBI), and details the process of creating an efficient and accurate intention recognition system. The domain chosen is chess. The system's objective was to discern the opponent's strategy. It is able to use the board positions and other relevant data of the current state to gain an understanding of the movement patterns of the opposition.

This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuzzy ARTMAP / Template-Based Interpretation Learning Engine. This algorithm attempts to achieve this learning task by constructing rules that govern the low-level context transitions made by the expert. By combining these rules with models for these low-level context behaviors, it is hypothesized that an intelligent model for the expert can be created that can adequately model his behavior. To evaluate FAMTILE, four testing scenarios were developed that attempt to achieve three distinct evaluation goals: assessing the learning capabilities of Fuzzy ARTMAP, evaluating the ability of FAMTILE to correctly predict expert actions and context choices given an observation, and creating a model of the expert's behavior that can perform the high-level task at a comparable level of proficiency.