Case-based reasoning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Repository Framework for Self-Growing Robot Software
APSEC '05 Proceedings of the 12th Asia-Pacific Software Engineering Conference
AlchemistJ: a framework for self-adaptive software
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
A semantically-based software component selection mechanism for intelligent service robots
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Policy-based self-adaptive architectures: a feasibility study in the robotics domain
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Autonomous functional configuration of a network robot system
Robotics and Autonomous Systems
Automatic Configuration of Multi-Robot Systems: Planning for Multiple Steps
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Policy-Based Architectural Adaptation Management: Robotics Domain Case Studies
Software Engineering for Self-Adaptive Systems
Model-Based extension of AUTOSAR for architectural online reconfiguration
MODELS'09 Proceedings of the 2009 international conference on Models in Software Engineering
Semantic based self-configuration approach for social robots in health care environments
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
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Behavioral, situational and environmental changes in complex software, such as robot software, cannot be completely captured in software design. To handle this dynamism, self-managed software enables its services dynamically adapted to various situations by reconfiguring its software architecture during run-time. We have developed a practical framework, called SHAGE (Self-Healing, Adaptive, and Growing SoftwarE), to support self-managed software for intelligent service robots. The SHAGE framework is composed of six main elements: a situation monitor to identify internal and external conditions of a software system, ontology-based models to describe architecture and components, brokers to find appropriate architectural reconfiguration patterns and components for a situation, a reconfigurator to actually change the architecture based on the selected reconfiguration pattern and components, a decision maker/learner to find the optimal solution of reconfiguring software architecture for a situation, and repositories to effectively manage and share architectural reconfiguration patterns, components, and problem solving strategies. We conducted an experiment of applying the framework to an infotainment robot. The result of the experiment shows the practicality and usefulness of the framework for the intelligent service robots.