Multilanguage hierarchical logics, or: How we can do without modal logics

Giunchiglia, Fausto; Serafini, Luciano

doi:10.1016/0004-3702(94)90037-x

Cited by 247 publications

(165 citation statements)

References 8 publications

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“…To address these questions we combine two different formal models from the field of Knowledge Representation and Reasoning: a conviviality model [1,2] and MultiContext Systems [3,4].…”

Section: How To Enhance Cooperative Distributed Information Systems (mentioning

confidence: 99%

Enhancing Cooperation in Distributed Information Systems Using Conviviality and Multi-Context Systems

Caire

Bikakis

2011

Lecture Notes in Computer Science

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Abstract. Modern information systems are characterized by the distribution of information and services among several autonomous heterogeneous entities. A major requirement for the success of such systems is that participating entities cooperate by sharing parts of their local knowledge. This paper presents a novel approach for modeling and enhancing cooperation in distributed information systems, which combines two formal models from the field of Knowledge Representation and Reasoning: a conviviality model and Multi-Context Systems. Our aim is two-fold. First, we develop a combined model for context-based representation and cooperation. Second, we provide the means for measuring cooperation leading to the design and evaluation of more convivial systems.

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“…To address these questions we combine two different formal models from the field of Knowledge Representation and Reasoning: a conviviality model [1,2] and MultiContext Systems [3,4].…”

Section: How To Enhance Cooperative Distributed Information Systems (mentioning

confidence: 99%

Enhancing Cooperation in Distributed Information Systems Using Conviviality and Multi-Context Systems

Caire

Bikakis

2011

Lecture Notes in Computer Science

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“…Multi-context systems were introduced by Giunchiglia et.al. [9] to allow different formal (logic) components to be defined and interrelated. The MCS specification of an agent contains three basic components: units or contexts, logics, and bridge rules, which channel the propagation of consequences among theories.…”

Section: Graded Bdi Agent Modelmentioning

confidence: 99%

“…In order to help in the design of such complex logical systems Giunchiglia et.al. [9] introduced the notion of multi-context system (MCS for short). This framework allows the definition of different formal components and their interrelation.…”

Section: Introductionmentioning

confidence: 99%

Graded BDI Models for Agent Architectures

Casali

Godo

Sierra

2005

Lecture Notes in Computer Science

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Abstract. In the recent past, an increasing number of multiagent systems (MAS) have been designed and implemented to engineer complex distributed systems. Several previous works have proposed theories and architectures to give these systems a formal support. Among them, one of the most widely used is the BDI agent architecture presented by Rao and Georgeff. We consider that in order to apply agents in real domains, it is important for the formal models to incorporate a model to represent and reason under uncertainty. With that aim we introduce in this paper a general model for graded BDI agents, and an architecture, based on multi-context systems, able to model these graded mental attitudes. This architecture serves as a blueprint to design different kinds of particular agents. We illustrate the design process by formalising a simple travel assistant agent.

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“…Based on McCarthy's idea of contextual reasoning [1], the Trento School around Giunchiglia and Serafini has developed multi-context systems in many works, in which the components (called contexts) can be interlinked via so called bridge rules for information exchange, cf. [2,3]. Generalizing this work, Brewka and Eiter [4] presented nonmonotonic multi-context systems (MCSs) as a generic framework for interlinking possibly heterogeneous and nonmonotonic knowledge bases.…”

Section: Introductionmentioning

confidence: 99%

Approximations for Explanations of Inconsistency in Partially Known Multi-Context Systems

Eiter

Fink

Schüller

2011

Logic Programming and Nonmonotonic Reasoning

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Abstract. Multi-context systems are a formalism to interlink decentralized and heterogeneous knowledge based systems (contexts), which interact via (possibly nonmonotonic) bridge rules. Inconsistency is a major problem, as it renders such systems useless. In addition, it is likely that complete knowledge about all system parts is unavailable and cannot be obtained, for instance in applications where confidentiality or trust are prohibitive. We therefore aim at explaining reasons for inconsistency in multi-context systems without having an omniscient view of the whole system. To this end we propose a representation for partial knowledge about contexts, and define over-and underapproximations for existing notions characterizing inconsistency in multi-context systems. Furthermore, we discuss query selection strategies for improving approximations in situations where a limited number of queries can be posed to a partially known context.

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Multilanguage hierarchical logics, or: How we can do without modal logics

Cited by 247 publications

References 8 publications

Enhancing Cooperation in Distributed Information Systems Using Conviviality and Multi-Context Systems

Enhancing Cooperation in Distributed Information Systems Using Conviviality and Multi-Context Systems

Graded BDI Models for Agent Architectures

Approximations for Explanations of Inconsistency in Partially Known Multi-Context Systems

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