Recognizing Teamwork Activity In Observations Of Embodied Agents

This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data

Autonomous Environmental Mapping In Multi-agent Uav Systems

UAV units are by many researchers and aviation specialists considered the future and cutting edge of modern flight technology. This thesis discusses methods for efficient autonomous environmental mapping in a multi-agent domain. An algorithm that emphasizes on team work by sharing the agents local map information and exploration intentions is presented as a solution to the mapping problem. General theories on how to model and implement rational autonomous behaviour for UAV agents are presented. Three different human and tactical behaviour modeling techniques are evaluated. The author found the CxBR paradigm to be the most interesting approach. Also, in order to test and quantify the theories presented in this thesis a simulation environment was developed. This simulation software allows for UAV agents to operate in a visual 3-D environment with mountains, other various terrain types, danger points and enemies to model unexpected events