Publications

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The large number of government and industry activities supporting the Unit of Action (UA), with attendant documents, reports and briefings, can overwhelm decision-makers with an overabundance of information that hampers the ability to make quick decisions often resulting in a form of gridlock. In particular, the large and rapidly increasing amounts of data and data formats stored on UA Advanced Collaborative Environment (ACE) servers has led to the realization that it has become impractical and even impossible to perform manual analysis leading to timely decisions. UA Program Management (PM UA) has recognized the need to implement a Decision Support System (DSS) on UA ACE. The objective of this document is to research the commercial Knowledge Discovery and Data Mining (KDDM) market and publish the results in a survey. Furthermore, a ranking mechanism based on UA ACE-specific criteria has been developed and applied to a representative set of commercially available KDDM solutions. In addition, an overview of four R&D areas identified as critical to the implementation of DSS on ACE is provided. Finally, a comprehensive database containing detailed information on surveyed KDDM tools has been developed and is available upon customer request.

Swarms of mobile robots can be tasked with searching a geographic region for targets of interest, sttch as buried land mines. We assume that the individual robots are equipped with sensors tuned to the targets of intercsi, that these sensors have limited range, and that the robots can communicate wilh one auolher to ennble conperatio,. I low can a swarm of cooperating sensate robols efficiently search a given geographic region far targets in lhe absence of a priori information about the targets' locations? Many of the “obvious” approaches are inefficient or lack robustness. One efficient approach is to have the robots traverse a spacefilling curve. For many geographic search applications, this method is energy-frugal, highly robust, and provides guaranteed coverage in a finite time that decreases as the reciprocal of the number of robots sharing the search task. Furthermore, control is inherently decentralized and requires very little robot-to-robot communication for the robots to organize their movements. This report presents some preliminary results from applying the Hilbert space-filling curve to geographic search by mobile robots.