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 field of Distributed Artificial 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 theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multi-Context Systems with non-monotonic features, such as local defeasible theories, defeasible mapping rules, and a preference ordering over the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through their mappings. We also provide an operational model in the form of a distributed algorithm for query evaluation, which is sound and complete with respect to the argumentation framework, as well as three alternative versions of the algorithm, each of which implements a different strategy for conflict resolution. The four strategies, which mainly differ in the type and extent of context and preference information that is used to resolve potential conflicts, have been evaluated in a simulated peer-to-peer system and implemented in Logic Programming in four different logic meta-programs.