Institute for Creative Technologies

The purpose of this paper is to develop a theoretical model of mitigation by integrating cognitive and discourse approaches to appraisal and coping. Mitigation involves strategic, emotional, linguistic, and Theory of Mind processes on different levels of consciousness. We emphasize that discourse analysis can assist our understanding of these processes.

MOUT infantry operations are among the most difficult tasks a soldier can perform.  MOUT training requires quick reactions, tight coordination, and significant amounts of close contact with friendly, enemy, and neutral forces. In a recently completed study for U.S. Army Soldier Systems Center RDECOM, a number of subject matter experts (SMEs) agreed that effective, deeply ingrained training with teammates is key to a successful MOUT mission.

While achieving realism has been a main goal in making convincing virtual reality (VR) environments, just what constitutes realism is still a question situated firmly in the research domain.  VR has become mature enough to be used in therapeutic applications such as clinical exposure therapy with some success. We now need detailed scientific investigations to better understand why VR works for these types of cases, and how it could work for other key applications such as training.  Just as in real life, it appears that the factors will be complex and multi-variate, and this plethoric situation presents exceptional challenges to the VR researcher. We would not want to lessen VR’s ability to replicate real world conditions in order to more easily study it, however, for by doing so we may compromise the very qualities that comprise its effectiveness.  What is really needed are more robust tools to instrument, organize, and visualize the complex data generated by measurements of participant experiences in a realistic virtual world.  We describe here our first study in an ongoing program of effective virtual environment research, the types of data we are dealing with, and a specific tool we have been compelled to create that allows us some measure of control over this data.  We call this tool Phloem, after the botanical channels that plants use to transport, support and store nutrients.

This paper presents a novel panning algorithm called Speaker-Placement Correction Amplitude Panning (SPCAP) which guarantees conservation of loudspeaker power output. The method is appropriate for any speaker arrangement (e.g. ITU 5.1, 10.2, etc.), and scales with the number of speakers. SPCAP works by correcting initial pan values based on speaker placement to achieve constant power output. Because panning occurs over an arbitrary number of speakers (i.e. is not pair-wise), SPCAP provides two significant advantages over discrete panning schemes. First, pan values for current and future surround-sound formats (e.g. 5.1 and 10.2) are guaranteed to conserve power under any lower-resolution setup, making dynamic up/down mixing in non-standard setups feasible. Second, SPCAP provides a framework for producing wide (non point-source) sounds.

This paper presents ARIA (Application Rendering Immersive Audio). This system provides a means for the research community to easily test and integrate algorithms into a multichannel playback/recording system. ARIA uses a host-based architecture, meaning that programs can be developed and debugged in standard C++ without the need for expensive, specialized DSP programming and testing tools. ARIA allows developers to exploit the speed and low cost of modern CPUs, provides cross-platform portability, and simplifies the modification and sharing of codes. This system is designed for real-time playback and processing, thus closing the gap between research testbed and delivery systems.

Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by incrementally using plan knowledge and observations to change state probabilities. We also discuss the work and compare it with other probabilistic models in the paper.

Social judgment is a process of social explanation whereby one evaluates which entities deserve credit or blame for multiagent activities. Such explanations are a key aspect of inference in a social environment and a model of this process can advance several design compo-nents of multi-agent systems. Social judgment underlies social planning, social learning, natural language prag-matics and computational model of emotion. Based on psychological attribution theory, this paper presents a computational approach to forming social judgment based on an agent’s causal knowledge and communica-tive interactions with other agents.

Fluid simulations are notoriously difficult to predict and control. As a result, authoring fluid flows often involves a tedious trial and error process. There is to date no convenient way of editing a fluid after it has been simulated. In particular, the Eulerian approach to fluid simulation is not suitable for flow editing since it does not provide a convenient spatio-temporal parameterization of the simulated flows. In this research, we develop a new technique to learn such parameterization. This technique is based on a new representation, the Advected Radial Basis Function. It is a time-varying kernel that models the local properties of the fluid. We describe this representation and demonstrate its use for interactive three-dimensional flow editing.

Data-driven approaches have been successfully used for realistic visual speech synthesis. However, little effort has been devoted to real-time lip-synching for interactive applications. In particular, algorithms that are based on a graph of motions are notorious for their exponential complexity. In this paper, we present a greedy graph search algorithm that yields vastly superior performance and allows real-time motion synthesis from a large database of motions. The time complexity of the algorithm is linear with respect to the size of an input utterance. In our experiments, the synthesis time for an input sentence of average length is under a second.

In an unbounded physical domain, simulating a turbulent fluid on an Eulerian grid is rather tricky. Since it is difficult to predict the motion of the fluid, it is also difficult to guess which computational domain would allow the simulation of the fluid without crossing the computational boundaries. To address this dilemma, we have developed a novel adaptive framework where the simulation grid follows the motion of the flow. Our technique is based onn the principle of Galilean Invariance and the culling of simulation cells using a metric derived from continuative boundary conditions. We describe our framework and showcase its advantages over traditional techniques. Timing results and visual comparisons are presented.