Name
Abstract | ||
|---|---|---|
A fault represents some erroneous operation of a system that could result from an action selection error or some abnormal condition. We formally define error models that characterize the likelihood of various faults and consider the problem of fault-tolerant planning, which optimizes performance given an error model. We show that factoring the possibility of errors significantly degrades the performance of stochastic planning algorithms such as LAO*, because the number of reachable states grows dramatically. We introduce an approach to plan for a bounded number of faults and analyze its theoretical properties. When combined with a continual planning paradigm, the k-fault-tolerant planning method can produce near-optimal performance, even when the number of faults exceeds the bound. Empirical results in two challenging domains confirm the effectiveness of the approach in handling different types of runtime errors. |
Year | Venue | Keywords |
|---|---|---|
2013 | IJCAI | error model,k-fault-tolerant planning method,bounded number,continual planning paradigm,stochastic planning,runtime error,abnormal condition,fault-tolerant planning,near-optimal performance,action selection error |
Field | DocType | Citations |
Planning algorithms,Computer science,Markov decision process,Fault tolerance,Artificial intelligence,Action selection,Factoring,Machine learning,Bounded function | Conference | 6 |
PageRank | References | Authors |
0.48 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Pineda | 1 | 16 | 4.85 |
| Yi Lu | 2 | 245 | 15.73 |
| Shlomo Zilberstein | 3 | 3419 | 255.70 |
| Claudia V. Goldman | 4 | 726 | 64.56 |