2013 35th International Conference on Software Engineering (ICSE) (2013)
San Francisco, CA, USA
May 18, 2013 to May 26, 2013
Daniel Sykes , Imperial College London, UK
Domenico Corapi , Imperial College London, UK
Jeff Magee , Imperial College London, UK
Jeff Kramer , Imperial College London, UK
Alessandra Russo , Imperial College London, UK
Katsumi Inoue , National Institute of Informatics, Tokyo, Japan
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since adaptive systems generally operate in environments which are subject to change, these models are often also out of date.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 nonmonotonic 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.
Adaptation models, Probabilistic logic, Adaptive systems, Planning, Robot sensing systems, Computational modeling
D. Sykes, D. Corapi, J. Magee, J. Kramer, A. Russo and K. Inoue, "Learning revised models for planning in adaptive systems," 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 2013, pp. 63-71.