Multi-Context Systems for Reactive Reasoning in Dynamic Environments
This work provides a reactive formalism for knowledge engineers to handle online reasoning in dynamic settings, though it appears incremental as it builds on existing multi-context systems.
The paper tackles the problem of continuous reasoning in dynamic environments by extending managed multi-context systems with sensors and defining runs for these systems, addressing issues like inconsistent sensor input and controlling reasoning effort, while investigating complexity and design choices.
We show in this paper how managed multi-context systems (mMCSs) can be turned into a reactive formalism suitable for continuous reasoning in dynamic environments. We extend mMCSs with (abstract) sensors and define the notion of a run of the extended systems. We then show how typical problems arising in online reasoning can be addressed: handling potentially inconsistent sensor input, modeling intelligent forms of forgetting, selective integration of knowledge, and controlling the reasoning effort spent by contexts, like setting contexts to an idle mode. We also investigate the complexity of some important related decision problems and discuss different design choices which are given to the knowledge engineer.