6140423
A multi-dimensional approach to force-directed layouts of large graphs
We present a novel hierarchical force-directed method for drawing large graphs. Given a graph G=(V.E), the algorithm produces an embedding for G in an Euclidean space E of any dimension. A two or three dimensional drawing of the graph is then obtained by projecting a higher-dimensional embedding into a two or three dimensional subspace of E. Such projections typically result in drawings that are "smoother" and more symmetric than direct drawings in 2D and 3D. 
 
In order to obtain fast placement of the vertices of the graph our algorithm employs a multi-scale technique based on a maximal independent set filtration of vertices of the graph. While most existing force-directed algorithms begin with an initial random placement of all the vertices, our algorithm attempts to place vertices "intelligently", close to their final positions. Other notable features of our approach include a fast energy function minimization strategy and efficient memory management. Our implementation of the algorithm can draw graphs with tens of thousands of vertices using a negligible amount of memory in less than one minute on a 550 MHz Pentium PC.
14677263
Teaching the analysis of algorithms with visual proofs
We describe an approach for visually teaching important proofs in the Junior-Senior level course on the design and analysis of data structures and algorithms (CS7/DS&amp;A). The main idea of this educational paradigm is to justify important claims about data structures and algorithms by using pictures that visualize proofs so clearly that the pictures can qualify as proofs themselves. The advantage of using this approach for DS&amp;A is that it augments or even replaces inductive arguments that many students find difficult. Moreover, this paradigm communicates important algorithmic facts in a compelling way for students who are more visually-oriented. We illustrate this technique by giving examples of visual proofs of several key concepts in DS&amp;A.
15210615
An Improved Ray Shooting Method for Constructive Solid Geometry Models Via Tree Contraction
In the Constructive Solid Geometry (CSG) representation a geometric object is described as the hierarchical combination of a number of primitive shapes using the operations union, intersection, subtraction, and exclusive-union. This hierarchical description defines an expression tree, T, called the CSG tree, with leaves associated with primitive shapes, internal nodes associated with operations, and whose "value" is the geometric object. Evaluation of CSG trees is an important computation that arises in many rendering and analysis problems for geometric models, with ray shooting (also known as "ray casting") being one of the most important. Given any CSG tree T, which may be unbalanced, we show how to convert T into a functionally-equivalent binary tree, D, that is balanced. We demonstrate the utility of this conversion by showing how it can be used to improve the worst-case running time for ray shooting against a CSG model from O(n2) to O(n log n), which is optimal. In addition, the practicality of our method has been demonstrated in experimental benchmarking tests using the BRL-CAD package.
1270229
Fast Randomized Parallel Methods for Planar Convex Hull Construction
Abstract We present a number of efficient parallel algorithms for constructing 2-dimensional convex hulls on a randomized CRCW PRAM. Specifically, we show how to build the convex hull of n presorted points in the plane in O(1) time using O(n log n) work, with n-exponential probability, or, alternately, in O ( log ∗ n) time using O(n) work, with n-exponential probability. We also show how to find the convex hull of n unsorted planar points in O(log n) time using O(n log h) work, with n-exponential probability, where h is the number of edges in the convex hull (h is O(n), but can be as small as O(1)). Our algorithm for unsorted inputs depends on the use of new in-place procedures, that is, procedures that are defined on a subset of elements in the input and that work without reordering the input. In order to achieve our n-exponential confidence bounds we use a new parallel technique called failure sweeping.
8676248
Authenticated Dictionaries for Fresh Attribute Credentials
We describe several schemes for efficiently populating an authenticated dictionary with fresh credentials. The thrust of this effort is directed at allowing for many data authors, called sources, to collectively publish information to a common repository, which is then distributed throughout a network to allow for authenticated queries on this information. Authors are assured of their contributions being added to the repository based on cryptographic receipts that the repository returns after performing the updates sent by an author. While our motivation here is the dissemination of credential status data from multiple credential issuers, applications of this technology also include time stamping of documents, document version integrity control, and multiple-CA certificate revocation management, to name just a few.
14228028
Efficient piecewise-linear function approximation using the uniform metric: (preliminary version)
We give an <italic>O</italic>(<italic>n</italic>log<italic>n</italic>)-time method for finding a best <italic>k</italic>-link piecewise-linear function approximating an <italic>n</italic>-point planar data set using the well-known uniform metric to measure the error, ε≥0, of the approximation. Our method is based upon new characterizations of such functions, which we exploit to design an efficient algorithm using a plane sweep in “ε space” followed by several applications of the parametric searching technique. The previous best running time for this problem was <italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>).
12614052
Learning haptic feedback for guiding driver behavior
Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.
451273
Comparing the usefulness of video and map information in navigation tasks
One of the fundamental aspects of robot teleoperation is the ability to successfully navigate a robot through an environment. We define successful navigation to mean that the robot minimizes collisions and arrives at the destination in a timely manner. Often video and map information is presented to a robot operator to aid in navigation tasks. This paper addresses the usefulness of map and video information in a navigation task by comparing a side-by-side (2D) representation and an integrated (3D) representation in both a simulated and a real world study. The results suggest that sometimes video is more helpful than a map and other times a map is more helpful than video. From a design perspective, an integrated representation seems to help navigation more than placing map and video side-by-side.
43673902
Coordinated target assignment and intercept for unmanned air vehicles
Presents an end-to-end solution to the battlefield scenario where M unmanned air vehicles are assigned to strike N known targets, in the presence of dynamic threats. The problem is decomposed into the subproblems of (1) cooperative target assignment, (2) coordinated UAV intercept, (3) path planning, and (4) feasible trajectory generation. The design technique is based on a hierarchical approach to coordinated control. Detailed simulation results are presented.
11363478
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining
Learning in many multi-agent settings is inherently repeated play. This calls into question the naive application of single play Nash equilibria in multi-agent learning and suggests, instead, the application of give-and-take principles of bargaining. We modify and analyze a satisficing algorithm based on (Karandikar et al., 1998) that is compatible with the bargaining perspective. This algorithm is a form of relaxation search that converges to a satisficing equilibrium without knowledge of game payoffs or other agents' actions. We then develop an M action, N player social dilemma that encodes the key elements of the Prisoner's Dilemma. This game is instructive because it characterizes social dilemmas with more than two agents and more than two choices. We show how several different multi-agent learning algorithms behave in this social dilemma, and demonstrate that the satisficing algorithm converges, with high probability, to a Pareto efficient solution in self play and to the single play Nash equilibrium against selfish agents. Finally, we present theoretical results that characterize the behavior of the algorithm.
1846400
Common metrics for human-robot interaction
This paper describes an effort to identify common metrics for task-oriented human-robot interaction (HRI). We begin by discussing the need for a toolkit of HRI metrics. We then describe the framework of our work and identify important biasing factors that must be taken into consideration. Finally, we present suggested common metrics for standardization and a case study. Preparation of a larger, more detailed toolkit is in progress.