Name
Abstract | ||
|---|---|---|
Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or inaccurate. In addition, since adaptive systems generally operate in environments which are subject to change, these models are often also out of date. To update and correct these models, the system should observe how the environment responds to its actions, and compare these responses to those predicted by the model. In this paper, we use a probabilistic rule learning approach, NoMPRoL, to update models using feedback from the running system in the form of execution traces. NoMPRoL is a technique for non-monotonic probabilistic rule learning based on a transformation of an inductive logic programming task into an equivalent abductive one. In essence, it exploits consistent observations by finding general rules which explain observations in terms of the conditions under which they occur. The updated models are then used to generate new behaviour with a greater chance of success in the actual environment encountered.
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Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICSE.2013.6606552 | Software Engineering |
Keywords | Field | DocType |
general rule,revised model,consistent observation,probabilistic rule,equivalent abductive,non-monotonic probabilistic rule,execution trace,adaptive system,actual environment,environment domain model,new behaviour,probabilistic logic,adaptive systems,computational modeling,learning artificial intelligence,planning,software architecture,feedback,machine learning | Inductive logic programming,Multi-task learning,Adaptive system,Computer science,Statistical relational learning,Inductive programming,Hyper-heuristic,Artificial intelligence,Probabilistic logic,Domain model,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-1-4673-3076-3 | 27 |
PageRank | References | Authors |
0.77 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Sykes | 1 | 207 | 8.26 |
| Domenico Corapi | 2 | 135 | 8.17 |
| Jeff Magee | 3 | 27 | 0.77 |
| Jeff Kramer | 4 | 7168 | 655.98 |
| Alessandra Russo | 5 | 1022 | 80.10 |
| Katsumi Inoue | 6 | 1271 | 112.78 |