Avneesh Sud

Real-Time Path Planning in Dynamic Virtual Environments Using Multiagent Navigation Graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multiagent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for the local dynamics computation of each agent by extending a social force model [15]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multiagent planning in pursuit-evasion, terrain exploration, and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

Fast Dynamic Voronoi Treemaps

The Voronoi Treemap is a space-filling treemap technique that relaxes the constraints of rectangular nodes. Its organic shapes maintain a one-to-one aspect ratio, are flexible with their placement, allowing stable zooming and dynamic data values. In this paper, we present algorithms for efficient computation and dynamic update of Voronoi Treemaps. Our GPGPU-based technique allows for rapid computation of centroidal Voronoi Diagrams, providing almost two orders of magnitude speedup over previous work. In addition, we present a hierarchical algorithm for stable updates. Finally, we demonstrate the application of Voronoi treemaps to real-world dynamic datasets, including interactive navigation.

Using information scent and need for cognition to understand online search behavior

The purpose of this study is to investigate the extent to which two theories, Information Scent and Need for Cognition, explain peoples search behaviors when interacting with search engine results pages (SERPs). Information Scent, the perception of the value of information sources, was manipulated by varying the number and distribution of relevant results on the first SERP. Need for Cognition (NFC), a personality trait that measures the extent to which a person enjoys cognitively effortful activities, was measured by a standardized scale. A laboratory experiment was conducted with forty-eight participants, who completed six open-ended search tasks. Results showed that while interacting with SERPs containing more relevant documents, participants examined more documents and clicked deeper in the search result list. When interacting with SERPs that contained the same number of relevant results distributed across different ranks, participants were more likely to abandon their queries when relevant documents appeared later on the SERP. With respect to NFC, participants with higher NFC paginated less frequently and paid less attention to results at lower ranks than those with lower NFC. The interaction between NFC and the number of relevant results on the SERP affected the time spent on searching and a participants likelihood to reformulate, paginate and stop. Our findings suggest evaluating system effectiveness based on the first page of results, even for tasks that require the user to view multiple documents, and varying interface features based on NFC.

Explicit feedback in local search tasks

Modern search engines make extensive use of peoples contextual information to finesse result rankings. Using a searchers location provides an especially strong signal for adjusting results for certain classes of queries where people may have clear preference for local results, without explicitly specifying the location in the query direct-ly. However, if the location estimate is inaccurate or searchers want to obtain many results from a particular location, they have limited control on the location focus in the search results returned. In this paper we describe a user study that examines the effect of offering searchers more control over how local preferences are gathered and used. We studied providing users with functionality to offer explicit relevance feedback (ERF) adjacent to results automatically identi-fied as location-dependent (i.e., more from this location). They can use this functionality to indicate whether they are interested in a particular search result and desire more results from that results location. We compared the ERF system against a baseline (NoERF) that used the same underlying mechanisms to retrieve and rank results, but did not offer ERF support. User performance was as-sessed across 12 experimental participants over 12 location-sensitive topics, in a fully counter-balanced design. We found that participants interacted with ERF frequently, and there were signs that ERF has the potential to improve success rates and lead to more efficient searching for location-sensitive search tasks than NoERF.