Approximately Efficient Online Mechanism Design

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility that agents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement efficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.Engineering and Applied Science

Interactive image-based rendering using feature globalization

Image-based rendering (IBR) systems enable virtual walkthroughs of photorealistic environments by warping and combining reference images to novel viewpoints under interactive user control. A significant challenge in such systems is to automatically compute image correspondences that enable accurate image warping

3D Reconstruction and Visualization Sea of Images A Dense Sampling Approach for Rendering Large Indoor Environments

Computer simulation of real-world environments is one of the great challenges of computer graphics. Ultimately, computer simulation technologies should let an untrained operator walk through a city or building with a handheld device that captures a digital model, which can provide the realistic visual experience of walking through the environment interactively. Applications include remote education, virtual heritage, specialist training, electronic commerce, and entertainment. For instance, students can “visit ” famous historical sites, archeologists can capture excavation sites as they evolve over time, and soldiers can train in simulated environments. The research challenge is to develop methods to capture, repre-The sea of images approach sent, and render large environments with photorealistic imagery from uses novel methods for arbitrary viewpoints at interactive rates. Navigation in these environ-capturing, rendering, and ments is critical, and, in many cases, the application should convey the managing large data sets to environment’s scale and duplicate its visual richness. For example, a provide interactive and virtual visit to the Louvre is not particularly interesting if only one room photorealistic walk-throughs is available, if the user is constrained to move along a preset sequence of of complex indoor viewpoints, or if the sculptures are displayed as synthetic renderings of environments. coarsely detailed 3D models. Current computer graphics techniques fall short of meeting these challenges. (See the “Related Work in Simulated Environments ” sidebar for a discussion of some current methods.) Our sea of images approach provides new methods for acquiring, analyzing, representing, and rendering photorealistic models of complex indoor environments. Figure 1 gives an overview of our image-based rendering (IBR) walk-through system based on the sea of images approach. In this article, we describe the system and give results for its implementation in three environments of different sizes and types. Although we’ve described several of the components i

Abstract

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility that agents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement ɛefficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.