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Brian C. Williams
Researcher at Massachusetts Institute of Technology
Publications - 254
Citations - 11118
Brian C. Williams is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Probabilistic logic & Computer science. The author has an hindex of 45, co-authored 236 publications receiving 10301 citations. Previous affiliations of Brian C. Williams include Ames Research Center & Vassar College.
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Time Resource Networks
TL;DR: This work introduces the Time Resource Network (TRN), an encoding for resource-constrained scheduling problems, and proposes two algorithms for determining the consistency of a TRN, one based on Mixed Integer Programing and the other based on Constraint Programming.
Proceedings ArticleDOI
Active detection of drivable surfaces in support of robotic disaster relief missions
TL;DR: This paradigm allows a single operator to manage several UAVs simultaneously and offers a feasible compromise between the two extremes of fully controlled and fully autonomous unmanned vehicles.
Book ChapterDOI
On-demand bound computation for best-first constraint optimization
TL;DR: This work introduces a method that generates - based on lazy, best-first variants of constraint projection and combination operators - only those bounds that are specifically required in order to generate a next best solution.
Journal ArticleDOI
A Hybrid Procedural/Deductive Executive for Autonomous Spacecraft
Barney Pell,Edward B. Gamble,Erann Gat,Ron Keesing,James Kurien,William Millar,Christian Plaunt,Brian C. Williams +7 more
TL;DR: The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft and to achieve this level of execution robustness, a procedural executive based on generic procedures with a deductive model-based executive is integrated.
Progress towards task-level collaboration between astronauts and their robotic assistants
TL;DR: This work develops a hybrid executive that can execute the tasks reliably, even while adapting to disturbances and execution uncertainties, in a task-level programming language that robots can directly interpret and understand.