Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Probabilistic Model Checking of Robots Deployed in Extreme Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use conservative Bayesian inference for catastrophic-failure transitions with failure-free data | Layered DTMC for the UUV · UUV valve-turning mission | estimated catastrophic transition probability | Helped |
| Use sets of priors for non-catastrophic transition parameters | Layered DTMC for the UUV · UUV valve-turning mission | transition-estimate uncertainty | Helped |
| Represent uncertain prior knowledge with sets of priors | Non-catastrophic transition estimates · UUV valve-turning mission | uncertainty under prior-data conflict | Mixed |
| Use a new observation that resolves prior-data conflict | Sets of priors estimator and runtime verification · UUV valve-turning mission, 101-th step | uncertainty in a1, R3, and R4 | Helped |
| Apply the framework using the stated priors and 49 previous missions | UUV valve-turning mission · Pre-mission verification | mission completion probability: [0.857,0.961] | Mixed |
| Apply the framework using the stated priors and 49 previous missions | UUV valve-turning mission · Pre-mission verification | catastrophic failure probability: [7.96\!\times\!10^{-4},1.55\!\times\!10^{-3}] | Mixed |
| Use imprecise priors so prior-data conflict widens posterior bounds | Sets of priors estimator · Transition estimates with conflicting prior and observed data | posterior interval width | Mixed |
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