(Georgia Institute of Technology, 2007-12-17)
Amundsen, Thomas Charles
In both the game industry and the academic community, there have not been many attempts to create complete interactive drama systems. I am working with a group that will be the first to undertake this daunting challenge, and one of the most important components in such a system is artificial intelligence (AI) to control the behaviors of non-player characters. Traditional approaches to designing the AI for any kind of interactive game involves designing characters who follow scripted behaviors. This method is cumbersome and amounts to gameplay that is repetitive and thus inhuman. This paper will describe my attempt to design a system that will allow an expert user to demonstrate behaviors to the system, which the system will use to learn how to behave on its own using case-based reasoning. The end result of this work will not only benefit interactive dramas but may help in the design of game AI for other genres of video games or simulations.
(Georgia Institute of Technology, 2007-12-17)
Wilson, Gregory
On the go computing is becoming more important for users who wish to access information from anywhere. Wearable computers are an optimal solution to achieving this feat because it allows for easy accessibility and quick use. There are many challenges that arise with small computers worn on the body. One of the most common issues is the interaction between the computer and the user and more specifically how the user enters input. In this paper we research a potential effective way to interact with a wrist watch by mounting touch sensors on the watch band.
(Georgia Institute of Technology, 2007-05)
Kramer, Christopher W.; Kolodner, Janet L.
The Learning by Design lab has been developing software and curriculum to support learning in the context of design challenges. We have used software to guide learners through design and investigation practices as well as help them describe their scientific observations. In this paper, we present easy-to-use software tools that support learners in their construction of robust science conceptions. We investigate how multiple representations such as diagrams, graphs, and animations can provide support for knowledge construction. In addition, we investigate how explanation templates scaffold learners’ interpretations of science. We have combined these tools into one software environment, Jacket’s Garage.
(Georgia Institute of Technology, 2007-05)
Kurjanowicz, Matthew David
Current Remote Direct Memory Access (RDMA) technologies are either not virtualization aware or run on proprietary connection fabrics such as InfiniBand, Myrinet and Quadrics. The introduction of 10-Gigabit Ethernet provides a high-speed connection that utilizes existing network infrastructure. As the first step to provide a useful, virtualization-aware RDMA implementation we propose an integrated OS- and VMM-bypass solution implemented on the Netronome NFE-i8000 Network Processor utilizing the Intel IXP 2855. We use this implementation to demonstrate the feasibility and usefulness of an integrated VMM- and OS-bypass communications engines that can be used to implement such a virtualized RDMA solution and to understand the architectural limitations of such an implementation.
(Georgia Institute of Technology, 2007-05)
Wood, Matthew
Computer software is constantly increasing in complexity; this requires more developer time, effort, and knowledge in order to correct bugs inevitably occurring in software production. Eventually, increases in complexity and size will make manually correcting programmatic errors impractical. Thus, there is a need for automated software-debugging tools that can reduce the time and effort required by the developer. The performance of previously developed debugging techniques can be greatly improved by combining them with machine-learning. Our research focuses on the application of neural networks within the domain of statistical debugging. Specifically, we develop methods to mine statistical debugging data that can then be used to train neural networks; these generated multi-layered neural networks can then be used to identify suspicious programmatic entities. Our developed networks are generated on a per program basis in order to leverage specific programmatic properties. In our empirical evaluation we compare our proposed approach with a state-of-the-art automated debugging technique. The results of the evaluation indicate that, for the cases considered, our approach is more effective than the considered technique.