Artificial intelligence, simulation & modeling
The Evolving Philosophers Problem: Dynamic Change Management
IEEE Transactions on Software Engineering
The B-book: assigning programs to meanings
The B-book: assigning programs to meanings
A Classification and Comparison Framework for Software Architecture Description Languages
IEEE Transactions on Software Engineering
The case for reflective middleware
Communications of the ACM - Adaptive middleware
The Vision of Autonomic Computing
Computer
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Computer
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Visibility of control in adaptive systems
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
PLASMA: a plan-based layered architecture for software model-driven adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Combining Proof and Model-checking to Validate Reconfigurable Architectures
Electronic Notes in Theoretical Computer Science (ENTCS)
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Considering a runtime compositional software adaptation, one important aspect about the engineering of self-adaptive systems is to decide how to reconfigure the software, that is, how to change its structure. After a desired configuration has been chosen for some context, the task is to decide how to achieve this desired state and, after that, execute this reconfiguration. Furthermore, this reconfiguration plan must be correct in relation to the programming model, the application architectural restrictions and, must leave the application in a consistent state. The plan execution must guarantee some level of reliability not to bring the system in an undesired state. In order to automate the reconfiguration, this work proposes an approach that employs Artificial Intelligence techniques to solve this issue. The reconfiguration plans are based on a model of cause and effect, which describes the set of reconfiguration actions from the underlying component model and, this component model provides a reflective support to execute the generated reconfiguration plan.