It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...

this paper we propose multi context systems (MC systems from now on) as a logical framework for the formal specification of complex reasoning. MC systems have been motivated and formally introduced in [6, 7]; they are also called multilanguage systems (ML systems) to emphasize the fact that they allow the definition of multiple languages, each language associated with a context. The general idea is to model local reasoning as deduction inside a context. A context is formally defined as an axiomatic formal system, i.e. a triple consisting of a language, a set of axioms and a set of inference rules. Interaction between contexts is formalized via bridge rules, i.e. rules whose premises and conclusion belong to different contexts. The notion of deduction in an MC system (modeling the reasoning of the whole system) is defined as the composition, via bridge rules, of the contextual deductions

In this paper we present a new notion of formal system, so called multilanguage system (ML-system) which allows the use of multiple distinct languages, each language being associated with its theory. ML-systems allow the use of inference rules, called bridge rules, whose premises and consequences need not belong to the same language. Bridge rules allow the propagation of results among theories, thus making them "partially" dependent on one another. Some examples of ML-systems are proposed and argued to formalize naturally and elegantly propositional attitudes and, in particular, belief.

We propose multi-context 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 sub-patterns. Each sub-pattern 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 paper we propose a three layer 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 multi-context 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 multi-context deductions.