A logic-based calculus of events
New Generation Computing
Model-based adaptation for self-healing systems
WOSS '02 Proceedings of the first workshop on Self-healing systems
An Architecture-Based Approach to Self-Adaptive Software
IEEE Intelligent Systems
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
From goals to components: a combined approach to self-management
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Rainbow: cost-effective software architecture-based self-adaptation
Rainbow: cost-effective software architecture-based self-adaptation
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Learning operational requirements from goal models
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
SEAMS '09 Proceedings of the 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
A connectionist cognitive model for temporal synchronisation and learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
PLASMA: a plan-based layered architecture for software model-driven adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
FUSION: a framework for engineering self-tuning self-adaptive software systems
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Synthesis of live behaviour models
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Synthesis of live behaviour models for fallible domains
Proceedings of the 33rd International Conference on Software Engineering
Learning to adapt requirements specifications of evolving systems (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
FlashMob: distributed adaptive self-assembly
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Probabilistic rule learning in nonmonotonic domains
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
Uncertainty handling in goal-driven self-optimization - Limiting the negative effect on adaptation
Journal of Systems and Software
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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.