Most approaches for adaptive systems rely on models, particularly behaviour or architecture models, which describe the system and the environment in which it operates. One of the difficulties in creating such models is uncertainty about the accuracy and completeness of the models. Engineers therefore make assumptions which may prove to be invalid at runtime. In this paper we introduce a rigorous, tiered framework for combining behaviour models, each with different associated assumptions and risks. These models are used to generate operational strategies, through techniques such controller synthesis, which are then executed concurrently at runtime. We show that our framework can be used to adapt the functional behaviour of the system: through graceful degradation when the assumptions of a higher level model are broken, and through progressive enhancement when those assumptions are satisfied or restored.

Controller synthesis provides an automated means to produce architecture-level behaviour models that are enacted by a composition of lower-level software components, ensuring correct behaviour. Such controllers ensure that goals are satisfied for any model-consistent environment behaviour. This paper presents a tool for developing environment models, synthesising controllers efficiently, and enacting those controllers using a composition of existing third-party components. Video: www.youtube.com/watch?v=RnetgVihpV4.

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since adaptive systems generally operate in environments which are subject to change, these models are often also out of date.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