Syeda Saleha Raza Dissertation Defense
You are cordially invited to attend PhD Defense of Syeda Saleha Raza, is scheduled on Nov 10, 2015 at FCS Board Room, IBA City Campus at 10:00 am.
TEAM LEARNING FROM DEMONSTRATION
A Framework to Build Collaboration in a Team of Agents via Imitation
This dissertation addresses the problem of building collaboration in a team of autonomous agents and presents imitation learning as an effective mechanism to build this collaboration. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism requires less time and technical expertise on behalf of domain experts/ knowledge engineers and makes it convenient for them to transfer knowledge to a software agent. The research extends the idea of a demonstration to multi-human demonstrations and presents a framework of Team Learning from Demonstration (TLfD) that allows a group of human experts to train a team of agents via demonstrations. To reduce the demonstration overhead, the dissertation emphasizes on a modular approach and enables the framework to train a team of a large number of agents via fewer numbers of demonstrators. The framework learns the collaborative strategy in the form of weighted naïve Bayes model where the parameters of the model are learned from the demonstration data and its weights are optimized using artificial immune system. The framework is thoroughly evaluated in the domain of RoboCup Soccer Simulation 3D which is a promising platform for a multi-agent domain and addresses many complex real-world problems. A series of experiments were conducted using Robocup Soccer in which the agents were trained to perform different types of tasks through TLfD framework. The experiments were started with training a single agent how to score a goal in an empty soccer field. The later experiments increased the complexity of the task and the number of agents involved. The final experiment eventually trains a full-fledged team 14 of 9 soccer players and enables them to play soccer against other competition quality teams. A number of test matches were played against different opponent teams, and the results of the matches were evaluated on the basis of different performance and behavioral metrics. The performance metrics describes how well the imitating team has played in the field whereas the behavioral metrics assesses how closely they have imitated the human demonstrations. Our soccer simulation 3D team KarachiKoalas serves as a benchmark to evaluate the quality of the imitating team, and we closely compare the two teams to see the effectiveness of TLfD framework.