Multi-Context Systems: Dynamics and Evolution

Lecture Notes in Computer Science, 2010

Multi-Context Systems provide a formal basis for the integration of knowledge from different knowledge sources. Yet, it is easy to conceive of applications where not all knowledge sources may be used together all the time. We present a natural extension of Multi-Context Systems by adding the notion of activation rules that allows modeling the applicability or relevance of contexts depending on beliefs in the various contexts and their mutual dependencies. We give a short account on possible consequence relations for Multi-Context System with Activation Rules and discuss a potential application in information retrieval.

Abstract. Multi-context systems have proven to be a powerful tool for formalizing complex logical problems in Artificial Intelligence, providing a flexible framework that allows the definition of different formal components and their interrelationships. Several MCS applications are oriented towards multiagent systems, in which several asynchronous tasks (inferences, messaging, etc.) are carried out.

2016

In this paper, we begin by introducing reactive multicontext systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources. In particular, we show how to integrate data streams into multi-context systems (MCSs) and how to model the dynamics of the systems, based on two types of bridge rules. We then discuss various methods for handling inconsistencies, a problem that occurs with reasoning based on multiple knowledge sources that need to be integrated, with a special focus on non-existence of equilibria.

Proceedings of the 23rd national conference on …, 2008

Multi-Context Systems (MCS) are logical formalizations of distributed context theories connected through a set of mapping rules, which enable information flow between different contexts. Reasoning in MCS introduces many challenges that arise from the heterogeneity of contexts with regard to the language and inference system that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. This study proposes a P2P reasoning model for MCS, which represents contexts as peer theories in a P2P system, mapping rules as defeasible rules (rules that can be defeated in the existence of adequate contrary evidence), and uses a preference relation (which, e.g., expresses trust information) to resolve the potential conflicts. It also provides a reasoning algorithm for query evaluation, analyzes its formal properties, and discusses alternative methods for conflict resolution, which differ in the type of information that they use to resolve the conflicts.

Ijis, 1995

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of "global" reasoning in terms of "local" reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. The global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a threelayer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. 0 1995 John Wiley & Sons, Inc.

2012

Abstract. Multi-Context Systems (MCS) are rule-based representation models for distributed, heterogeneous knowledge sources, called contexts, such as ambient intelligence devices and agents. Contexts interact with each other through the sharing of their local knowledge, or parts thereof, using so-called bridge rules to enable the cooperation among different contexts.

Frontiers in Public Health

In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different con...

arXiv (Cornell University), 2015

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 nontrivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria.

IEEE Transactions on Knowledge and Data Engineering, 2000

Lecture Notes in Computer Science, 2014

Multi-context systems (MCSs) are an important framework for heterogeneous combinations of systems within the Semantic Web. In this paper, we propose generic constructions to achieve specific forms of interaction in a principled way, and systematize some useful techniques to work with ontologies within an MCS. All these mechanisms are presented in the form of general-purpose design patterns. Their study also suggests new ways in which this framework can be further extended.