Bradley, Elizabeth


Forensic Reasoning about Paleoclimatology

AAAI Conferences

Human experts in many scientific fields routinely work with data that are heterogeneous, noisy, and/or uncertain, as well as heuristics that are unproven and possible conclusions that are contradictory. We present a deployed software system for cosmogenic isotope dating, a domain that is fraught with these difficult issues. This system, which is called ACE (“age calculation engine”), takes as inputs the nuclide densities in a set of rock samples taken from a landform. It reasons from these data — which capture how long those rocks have been exposed to the sky — to answer the scientific question “What geological processes could have produced this distribution of nuclide concentrations, and over what time scales?” To do this, it employs an encoded knowledge base of the possible processes that may have acted on that landform in the past, complete with the mathematics of how those processes can affect samples, and it uses a workflow system to encode the computations associated with this scientific analysis. The system remains in active use to this day; the project website (ace.hwr.arizona.edu) has received over 17,000 hits since 2008 and the software (∼20,000 lines of python code) has been downloaded nearly 600 times as of April 2013, which is a significant number in a research community of O(102) PI-level scientists.


Intelligent Computation of Reachability Sets for Space Missions

AAAI Conferences

This paper introduces a new technique for intelligently exploring the reachability set of a spacecraft: the set of trajectories from a given initial condition that are possible under a specified range of control actions. The high dimension of this problem and the nonlinear nature of gravitational interactions make the geometry of these sets complicated, hard to compute, and all but impossible to visualize. Currently, exploration of a problem’s state space is done heuristically, based on previously identified solutions. This potentially misses out on improved mission design solutions that are not close to previous approaches. The goal of the work described here is to map out reachability sets automatically. This would not only aid human mission planners, but also allow a spacecraft to determine its own course without input from Earth-based controllers. Brute-force approaches to this are computationally prohibitive, so one must focus the effort on regions that are of interest: where neighboring trajectories diverge quickly, for instance, or come close to a body that the spacecraft is orbiting. In this paper, we focus on the first of those two criteria; the goal is to identify regions in the system’s state space where small changes have large effects— or vice versa—and concentrate the computational mesh accordingly.


Strange Beta: An Assistance System for Indoor Rock Climbing Route Setting Using Chaotic Variations and Machine Learning

arXiv.org Artificial Intelligence

This paper applies machine learning and the mathematics of chaos to the task of designing indoor rock-climbing routes. Chaotic variation has been used to great advantage on music and dance, but the challenges here are quite different, beginning with the representation. We present a formalized system for transcribing rock climbing problems, then describe a variation generator that is designed to support human route-setters in designing new and interesting climbing problems. This variation generator, termed Strange Beta, combines chaos and machine learning, using the former to introduce novelty and the latter to smooth transitions in a manner that is consistent with the style of the climbs This entails parsing the domain-specific natural language that rock climbers use to describe routes and movement and then learning the patterns in the results. We validated this approach with a pilot study in a small university rock climbing gym, followed by a large blinded study in a commercial climbing gym, in cooperation with experienced climbers and expert route setters. The results show that {\sc Strange Beta} can help a human setter produce routes that are at least as good as, and in some cases better than, those produced in the traditional manner.


Water Conservation Through Facilitation on Residential Landscapes

AAAI Conferences

Plants can have positive effects on each other in numerous ways, including protection from harsh environmental conditions. This phenomenon, known as facilitation, occurs in water-stressed environments when shade from larger shrubs protects smaller annuals from harsh sun, enabling them to exist on scarce water. The topic of this paper is a model of this phenomenon that allows search algorithms to find residential landscape designs that incorporate facilitation to conserve water. This model is based in botany; it captures the growth requirements of real plant species in a fitness function, but also includes a penalty term in that function that encourages facilitative interactions with other plants on the landscape. To evaluate the effectiveness of this approach, two search strategies--simulated annealing and agent-based search--were applied to models of different collections of simulated plant types and landscapes with different light distributions. These two search strategies produced landscape designs with different spatial distributions of the larger plants. All designs exhibited facilitation and lower water use than designs where facilitation was not included.


Providing Decision Support for Cosmogenic Isotope Dating

AI Magazine

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a deployed AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments.


Providing Decision Support for Cosmogenic Isotope Dating

AI Magazine

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a deployed AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.


Providing Decision Support for Cosmogenic Isotope Dating

AAAI Conferences

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a fully implemented AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.




Review of The Computational Beauty of Nature

AI Magazine

A review of "The Computational Beauty of Nature: Computer Exploration of Fractals, Chaos, Complex Systems, and Adaptation, by Gary William Flake. Cambridge, Mass.: The MIT Press, 1998.