Brian C. Williams
Massachusetts Institute of Technology
254 Papers
2K Citations
Brian C. Williams is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Probabilistic logic. The author has an hindex of 45, co-authored 236 publications. Previous affiliations of Brian C. Williams include Ames Research Center & Vassar College.
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Papers
Diagnosing multiple faults
J. de Kleer,Brian C. Williams +1 more
TL;DR: The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device.
2.3K
Remote Agent: to boldly go where no AI system has gone before
TL;DR: The Remote Agent is described, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future of space exploration.
780
•Proceedings Article
Diagnosis with behavioral modes
Johan de Kleer,Brian C. Williams +1 more
- 20 Aug 1989
TL;DR: A general diagnostic theory is presented that uses the perspective of diagnosis as ideniifying consisieni modes of behavior, correct or faulty, to identify faulty components without necessarily knowing how they fail.
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
TL;DR: In this paper, the authors present a method for chance-constrained predictive stochastic control of dynamic systems, which takes into account uncertainty to ensure that the probability of failure due to collision with obstacles, for example, is below a given threshold.
Improved human-robot team performance using chaski, a human-inspired plan execution system
Julie A. Shah,James Wiken,Brian C. Williams,Cynthia Breazeal +3 more
- 06 Mar 2011
TL;DR: Chaski is a task-level executive that enables a robot to collaboratively execute a shared plan with a person, and it is shown that Chaski reduces the human's idle time by 85%, a statistically significant difference that supports the hypothesis that human-robot team performance is improved when a robot emulates the effective coordination behaviors observed in human teams.
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