MultiLanguage systems (ML systems) are formal systems allowing the use of multiple distinct logical languages. In this paper we introduce a class of ML systems which use a hierarchy of first order languages, each language containing names for the language below, and propose them as an alternative to modal logics. The motivations of our proposal are technical, epistemological and implementational. From a technical point of view, we prove, among other things, that the set of theorems of the most common modal logics can be embedded (under the obvious bijective mapping between a modal and a first order language) into that of the corresponding ML systems. Moreover, we show that ML systems have properties not holding for modal logics and argue that these properties are justified by our intuitions. This claim is motivated by the study of how ML systems can be used in the representation of beliefs (more generally, propositional attitudes) and provability, two areas where modal logics have been extensively used. Finally, from an implementation point of view, we argue that ML systems resemble closely the current practice in the computer representation of propositional attitudes and metatheoretic theorem proving.

Abstract. The paper addresses the problem of reasoning with multiple ontologies interrelated with semantic mappings. This problem is becoming more and more relevant due to the necessity of building a scalable ontological reasoning tools for the Semantic Web. In contrast to the so called global approach, in which reasoning with multiple semantically related ontologies is performed in a global knowledge base that encodes both ontologies and semantic mappings, we propose a distributed reasoning approach in which reasoning is the result of combination via semantic mappings of local reasonings chunks performed in single ontologies. The paper presents a tableau-based distributed reasoning procedure which is sound and complete w.r.t. Distributed Description Logics, the formal framework used to represent multiple semantically connected ontologies. The paper also describes the design and implementation principles of a distributed reasoning system, called DRAGO (Distributed Reasoning Architecture for a Galaxy of Ontology), that implements such distributed decision procedure. 1

As discussed in previous papers, belief contexts are a powerful and appropriate formalism for the representation and implementation of propositional attitudes in a multiagent environment. In this paper we show that a formalization using belief contexts is also elaboration tolerant. That is, it is able to cope with minor changes to input problems without major revisions. Elaboration tolerance is a vital property for building situated agents: it allows for adapting and re-using a previous problem representation in different (but related) situations, rather than building a new representation from scratch. We substantiate our claims by discussing a number of variations to a paradigmatic case study, the Three Wise Men problem. Introduction Belief contexts (Giunchiglia 1993; Giunchiglia & Serafini 1994; Giunchiglia et al. 1993) are a formalism for the representation of propositional attitudes. Their basic feature is modularity: knowledge can be distributed into different and separated mod...

: Our ultimate goal is to provide a framework and a methodology which will allow users, and not only system developers, to construct complex reasoning systems by composing existing modules, or to add new modules to existing systems, in a "plug and play" manner. These modules and systems might be based on different logics; have different domain models; use different vocabularies and data structures; use different reasoning strategies; and have different interaction capabilities. This paper makes two main contributions towards our goal. First, it proposes a general architecture for a class of reasoning systems called Open Mechanized Reasoning Systems (OMRSs). An OMRS has three components: a reasoning theory component which is the counterpart of the logical notion of formal system, a control component which consists of a set of inference strategies, and an interaction component which provides an OMRS with the capability of interacting with other systems, including OMRSs and hum...

Context-sensitivity is an important characteristic feature of every cognitive process and therefore should be reflected in every architecture pretending to explain human cognition. In this paper some experimental facts demonstrating context effects on various cognitive processes are reviewed and an attempt at context modeling is described. A hybrid (symbolic/connectionist) cognitive architecture, DUAL, is proposed. It consists of a multitude of agents having both a symbolic and a connectionist part. The symbolic part represents some knowledge structure, while the connectionist part represents its relevance to the current context. The performance of the cognitive system emerges as result of the work and interaction of the currently active agents, where the set of active agents is not predefined for a specific task but is dynamic and reflects the specific context. So particular symbolic operations and data structures may be supported or suppressed depending on the particu...

become a new fashioned managerial practice. Though KM theories seem to benefit from a "contamination" with cognitive and social sciences, which emphasize a subjective, contextual, and distributed approach to knowledge representation and integration, current technologies support what we may call a "god's eye" paradigm, in which knowledge is viewed as an objective resource. In this paper we discuss artificial intelligence theories and technologies that can support a shift to a new paradigm, called the "distributed intelligence" paradigm, in designing KM systems. Using the evolution of KM systems within Arthur Andersen Consulting as a motivating case study, we propose the framework of MultiContext Systems as a specification language for distributed intelligence KM systems, and sketch an agent-based architecture as an example of a KM system which embodies the assumptions of the distributed intelligence paradigm.

Context appears in Artificial Intelligence (AI) as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, such as Natural Language Processing, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits us to have a clear view of the context in AI. One of the conclusions of this survey is to point out the existence of different types of context along knowledge representation, the mechanisms of reasoning on the knowledge, and the interaction of the computer system with humans.

In the area of agent-based computing there are many proposals for specific system architectures, and a number of proposals for general approaches to building agents. As yet, however, there are comparatively few attempts to relate these together, and even fewer attempts to provide methodologies which relate designs to architectures and then to executable agents. This paper provides a first attempt to address this shortcoming. We propose a general method of specifying logic-based agents, which is based on the use of multi-context systems, and give examples of its use. The resulting specifications can be directly executed, and we discuss an implementation which makes this direct execution possible.

This paper presents the issues of dealing with context from the perspective of cognitive modeling. A dynamic theory of context is proposed which considers context as the set of all entities that influence human cognitive behavior on a particular occasion. As a consequence context is thought of as the dynamic fuzzy set of all associatively relevant memory elements at a particular instant of time. These memory elements might be both mental representations and operations. Some experimental facts about the influence of the perceptible environment as well as of the previous memory state on human problem solving are briefly presented. The dynamic nature of context influence on behavior is emphasized. A general cognitive architecture, DUAL, is presented which consists of many small agents running autonomously in parallel with variable speeds depending on their current associative relevance. A model of problem solving, AMBR, based on DUAL is discussed where problem solving emerges ...

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