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High-probability grants
According to our matching algorithm, George F. Luger is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 1998 — 2002 |
Luger, George |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Constructing Diagnostic Explanations Using Schema-Structured Bayesian Networks @ University of New Mexico
The goal of this research is to develop a representation and algorithms that characterize diagnostic reasoning. Human experts in a problem domain frequently interpret data in that domain in the context of a particular causal hypothesis. This type reasoning contrasts with deductive inference where from a set of general rules and facts further information is deduced by sound inference rules. Diagnostic reasoning, often called abductive inference, however, moves from a set of facts to the "best explanation" for the existence of these facts. This process often requires the expert to make a hypothetical conjecture that would explain the facts and then search for specific new information that can confirm that conjecture. Thus the human expert searches through a space of possible explanations for the observed information. This research uses Bayesian Belief Networks to build causal models of a domain. This approach represents in a precise way the interrelationship of causal patterns and their use in moving towards an explanation. The research supports handling of conflicting and ambiguous evidence, as well as a clear method for rating plausible inferences and the possibility of learning relative strengths of conditional probabilities from available statistical data. Although the research domain is built on data from investigation of failures of discrete component semiconductors as well as the analysis of failure mechanisms in complex real time control, diagnostic reasoning is a general research area. Results could be important in modeling medical decision-making, integrated circuit fault analysis, as well as used in real time process monitoring and control. http://www.cs.unm.edu/CS_Dept/faculty/homepage/luge r/
|
0.915 |
| 1999 — 2002 |
Luger, George |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
U.S.-U.K. Cooperative Research: Constructing Diagnostic Explanations Using Schema-Structured Bayesian Networks @ University of New Mexico
9900485
Luger
This three-year award supports US-UK collaborative research in knowledge and cognitive systems between George Luger of the University of New Mexico and Brendan McGonigle at the University of Edinburgh's Laboratory for Cognitive Neuroscience and Intelligent Systems. Their research involves the development of probabilistic models of diagnostic reasoning and testing of new algorithms developed by the US investigator. Both groups are concerned with performance improvement of robots through error identification and failure recovery. The investigators propose that iterative performance will improve by embedding abductive reasoning in the robot's environment.
The US investigator brings to this collaboration expertise in schema-based abduction, a form of causal reasoning, which employs Bayesian Networks as the underlying representation. This is complemented by the Edinburgh group's expertise in intelligent systems and takes advantage of learning experiments utilizing their NOMAD robot. Collaboration with the Edinburgh group provides an opportunity to study the cognitive plausibility of the US investigator's proposed model of diagnostic reasoning.
|
0.915 |