Privacy Preserving Multiagent Probabilistic Reasoning about Ambiguous Contexts: A Case Study

Contexts in ubiquitous environments, either sensed or interpreted, are usually ambiguous. However, to provide context-aware services and applications, agents in the environments need to have an as clear as possible understanding of their contexts. Ambiguous contexts can be made clearer by agents using inference based on their domain knowledge, local and global evidence. Bayesian networks have been proposed to represent and reason about uncertain contexts under the single agent paradigm. In distributed multiagent systems, multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for distributed multiagent probabilistic inference, where agents' privacy is respected. In this paper, we propose to apply MSBNs to uncertain contexts representation and reasoning in ubiquitous environments