Antonis Bikakis, University of Crete Examination Committee:

Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges in the eld of Distributed Articial Intelligence, which have not yet been successfully addressed by current Ambient Intelligence systems. This thesis proposes a solution based on the Multi-Context Systems paradigm, in which local context knowledge of ambient agents is encoded in rule the-ories (contexts), and information ow between agents is achieved through mapping rules that associate concepts used by dierent contexts. To handle imperfect context, we extend Multi-Context Systems with non-monotonic features, such as local defeasible theories, defeasible mapping rules, and