Learning revised models for planning in adaptive systems

• Published in: Proceedings of the 2013 International Conference on Software Engineering pp. 63 - 71
• Main Authors: Sykes, Daniel, Corapi, Domenico, Magee, Jeff, Kramer, Jeff, Russo, Alessandra, Inoue, Katsumi
• Format: Conference Proceeding
• Language: English
• Published: Piscataway, NJ, USA IEEE Press 18.05.2013
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning; Computing methodologies > Modeling and simulation > Model development and analysis; Software and its engineering > Software creation and management > Designing software > Software implementation planning > Software design techniques; Software and its engineering > Software creation and management > Software development process management
• ISBN: 1467330760; 9781467330763
• DOI: 10.5555/2486788.2486797

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since adaptive systems generally operate in environments which are subject to change, these models are often also out of date.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.