On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)
This addresses a foundational challenge in dynamic knowledge integration for AI systems, but it is incremental as it builds on existing eMCS frameworks.
The paper tackles the problem of defining minimal change in evolving Multi-Context Systems (eMCSs), which integrate heterogeneous knowledge representations in dynamic scenarios, and discusses alternative criteria for this notion.
Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. The notion of minimal change is a central notion in dynamic scenarios, specially in those that admit several possible alternative evolutions. Since eMCSs combine heterogeneous KR formalisms, each of which may require different notions of minimal change, the study of minimal change in eMCSs is an interesting and highly non-trivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria.