Evolving Multi-Context Systems

被引：17

作者：

Goncalves, Ricardo ^{[1
]}

Knorr, Matthias ^{[1
]}

Leite, Joao ^{[1
]}

机构：

[1] Univ Nova Lisboa, Fac Ciencias & Tecnol, CENTRIA, P-1200 Lisbon, Portugal

来源：

21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014) | 2014年 / 263卷

关键词：

D O I：

10.3233/978-1-61499-419-0-375

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. In this paper, we introduce evolving Multi-Context Systems (eMCSs), a general and flexible framework which inherits from mMCSs the ability to integrate knowledge represented in heterogeneous KR formalisms, and at the same time is able to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. We show that eMCSs are indeed very general and expressive enough to capture several existing KR approaches that model dynamics of knowledge.

引用

页码：375 / 380

页数：6

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