Arrow Research search

Author name cluster

David Thompson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers
2 author rows

Possible papers

9

AAAI Conference 2017 Conference Paper

The Positronic Economist: A Computational System for Analyzing Economic Mechanisms

  • David Thompson
  • Neil Newman
  • Kevin Leyton-Brown

Computational mechanism analysis is a recent approach to economic analysis in which a mechanism design setting is analyzed entirely by a computer. For games with non-trivial numbers of players and actions, the approach is only feasible when these games can be encoded compactly, e. g. , as Action-Graph Games. Such encoding is currently a manual process requiring expert knowledge; our aim is to simplify and automate it. Our contribution, the Positronic Economist is a software system having two parts: (1) a Python-based language for succinctly describing mechanisms; and (2) a system that takes such descriptions as input, automatically identifies computationally useful structure, and produces a compact Action-Graph Game.

PDF Details

AAAI Conference 2015 Conference Paper

Spatio-Spectral Exploration Combining In Situ and Remote Measurements

  • David Thompson
  • David Wettergreen
  • Greydon Foil
  • Michael Furlong
  • Anatha Kiran

Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on highdimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.

PDF Details

AAAI Conference 2013 Conference Paper

Guiding Scientific Discovery with Explanations Using DEMUD

  • Kiri Wagstaff
  • Nina Lanza
  • David Thompson
  • Thomas Dietterich
  • Martha Gilmore

In the era of large scientific data sets, there is an urgent need for methods to automatically prioritize data for review. At the same time, for any automated method to be adopted by scientists, it must make decisions that they can understand and trust. In this paper, we propose Discovery through Eigenbasis Modeling of Uninteresting Data (DEMUD), which uses principal components modeling and reconstruction error to prioritize data. DEMUD’s major advance is to offer domain-specific explanations for its prioritizations. We evaluated DE- MUD’s ability to quickly identify diverse items of interest and the value of the explanations it provides. We found that DEMUD performs as well or better than existing class discovery methods and provides, uniquely, the first explanations for why those items are of interest. Further, in collaborations with planetary scientists, we found that DEMUD (1) quickly identifies very rare items of scientific value, (2) maintains high diversity in its selections, and (3) provides explanations that greatly improve human classification accuracy.

PDF Details

AAAI Conference 2012 Conference Paper

Approximately Revenue-Maximizing Auctions for Deliberative Agents

  • L. Celis
  • Anna Karlin
  • Kevin Leyton-Brown
  • C. Nguyen
  • David Thompson

In many real-world auctions, a bidder does not know her exact value for an item, but can perform a costly deliberation to reduce her uncertainty. Relatively little is known about such deliberative environments, which are fundamentally different from classical auction environments. In this paper, we propose a new approach that allows us to leverage classical revenue-maximization results in deliberative environments. In particular, we use Myerson (1981) to construct the first nontrivial (i. e. , dependent on deliberation costs) upper bound on revenue in deliberative auctions. This bound allows us to apply existing results in the classical environment to a deliberative environment. In addition, we show that in many deliberative environments the only optimal dominant-strategy mechanisms take the form of sequential posted-price auctions.

PDF Details

AAMAS Conference 2012 Conference Paper

Decentralized Active Robotic Exploration and Mapping for Probabilistic Field Classification in Environmental Sensing

  • Kian Hsiang Low
  • Jie Chen
  • John Dolan
  • Steve Chien
  • David Thompson

A central problem in environmental sensing and monitoring is to classify/label the hotspots in a large-scale environmental field. This paper presents a novel \emph{decentralized active robotic exploration} (DARE) strategy for probabilistic classification/labeling of hotspots in a \emph{Gaussian process} (GP)-based field. In contrast to existing state-of-the-art exploration strategies for learning environmental field maps, the time needed to solve the DARE strategy is independent of the map resolution and the number of robots, thus making it practical for in situ, real-time active sampling. Its exploration behavior exhibits an interesting formal trade-off between that of boundary tracking until the hotspot region boundary can be accurately predicted and wide-area coverage to find new boundaries in sparsely sampled areas to be tracked. We provide a theoretical guarantee on the active exploration performance of the DARE strategy: under reasonable conditional independence assumption, we prove that it can optimally achieve two formal cost-minimizing exploration objectives based on the misclassification and entropy criteria. Importantly, this result implies that the uncertainty of labeling the hotspots in a GP-based field is greatest at or close to the hotspot region boundaries. Empirical evaluation on real-world plankton density and temperature field data shows that, subject to limited observations, DARE strategy can achieve more superior classification of hotspots and time efficiency than state-of-the-art active exploration strategies.

PDF

TIST Journal 2012 Journal Article

Surface Sulfur Detection via Remote Sensing and Onboard Classification

  • Lukas Mandrake
  • Umaa Rebbapragada
  • Kiri L. Wagstaff
  • David Thompson
  • Steve Chien
  • Daniel Tran
  • Robert T. Pappalardo
  • Damhnait Gleeson

Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface sulfur deposits. These deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the sulfur could not be detected by simply matching observations to sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful sulfur detection. Our findings include (1) the Borup Fiord sulfur deposits were best modeled as containing two sub-populations: sulfur on ice and sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.

Details DOI

AAAI Conference 2011 Conference Paper

Dominant-Strategy Auction Design for Agents with Uncertain, Private Values

  • David Thompson
  • Kevin Leyton-Brown

We study the problem of designing auctions for agents who incur a cost if they choose to learn about their own preferences. We reformulate the revelation principle for use with such deliberative agents. Then we characterize the set of single-good auctions giving rise to dominant strategies for deliberative agents whose values are independent and private. Interestingly, this set of dominant-strategy mechanisms is exactly the set of sequential posted-price auctions, a class of mechanisms that has received much recent attention.

PDF Details

AAAI Conference 2010 Conference Paper

Comparing Position Auctions Computationally

  • David Thompson
  • Kevin Leyton-Brown

Modern techniques for representing games and computing their Nash equilibria are approaching the point where they can be used to analyze market games. We demonstrate this by showing how the equilibria of different position auction mechanisms can be tractably identified using these techniques. These results enable detailed and quantitative comparisons of the different auction mechanisms—in terms of both efficiency and revenue— under different preference models and equilibrium selection criteria.

PDF Details

ICRA Conference 1987 Conference Paper

Conceptual development of an adaptive real-time seam tracker for welding automation

  • Nitin Nayak
  • David Thompson
  • Asok Ray
  • Andrew Vavreck

The paper presents the concept of a real-time seam tracking system for welding automation and the initial design for building a prototype. The ARTIST - Adaptive Real-Time Intelligent Seam Tracker - uses a robot-held, laser based vision system for automation of arc welding processes, and is currently under development at the Applied Research Laboratory of The Pennsylvania State University. The design of ARTIST builds upon the concept of a zero-pass technique where 3D information of the seam geometry is collected and processed for real time guidance and control of the welding torch trailing behind the laser-based vision sensor. This zero-pass concept eliminates the need for pre-programming of the weld path and thus potentially enhances the welding cycle time for small batches. The ARTIST is designed to support multipass arc welding and to handle any tack welds which are encountered during the seam welding operation.

Details