Developing real-time knowledge-based systems with PERFECT

PERFECT (Programming EnviRonment For Expert systems Constrained in reasoning Time) is aimed at providing the necessary engineering support in real-time knowledge-based system development. PERFECT bridges the gap between the traditional analysis and design methodologies, and the implementation tools for these systems. It does so by providing the means to construct a knowledge model and to choose a suitable inference strategy. Subsequently the properties of the knowledge model and inference strategy may be analysed. For instance, it may be checked whether the knowledge model contains sufficient knowledge to diagnose a fault in an industrial process. Moreover, it may be checked whether the inference engine is able to provide an answer to a certain problem in time. If not, the analyser of PERFECT proposes an alternative structure of the knowledge model. When the constructed knowledge model and the chosen inference strategy show the required time efficiency, the compiler of PERFECT may translate them to an actual real-time knowledge based system in COGSYS. In addition, guidelines are provided with respect to the design of the human-machine interface. The resulting system is an instrument—a source of information that can be used by the human operator during problem-solving, rather than a prosthesis—a device that solves the entire problem by itself and presents the outcome to the human operator.     

计算机生成兵力中的人工智能技术

开发CGF的难点和重点在于CGF实体行为的生成,尤其是人类智能行为的实现。人类行为建模最重要的三个方面是知识获取、知识表示和决策机制,都与人工智能技术相关。该文主要介绍人类行为描述的知识表示和知识获取问题,讨论了人工智能技术在这两个方面的应用,并对将来的研究工作做了一个简单的展望。     

Automated Support for Building and Extending Expert Models

Building a knowledge-based system is like developing a scientific theory. Although a knowledge base does not constitute a theory of some natural phenomenon, it does represent a theory of how a class of professionals approaches an application task. As when scientists develop a natural theory, builders of expert systems first must formulate a model of the behavior that they wish to understand and then must corroborate and extend that model with the aid of specific examples. Thus there are two interrelated phases of knowledge-base construction: (1) model building and (2) model extension. Computer-based tools can assist developers with both phases of the knowledge-acquisition process. Workers in the area of knowledge acquisition have developed computer-based tools that emphasize either the building of new models or the extension of existing models. The PROTÉGÉ knowledge-acquisition system addresses these two activities individually and facilitates the construction of expert systems when the same general model can be applied to a variety of application tasks.     

C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Efficient design of SIW filters with knowledge‐embedded space mapping technique

This article presents an optimization technique for the design of substrate‐integrated waveguide (SIW) filters using knowledge‐embedded space mapping. An effective coarse model is proposed to represent the SIW filter. The proposed coarse model can be analyzed in the available commercial software ADS. The embedded knowledge includes not only formulas but also extracted design curves, which help to build the mapping between the coarse and fine models. The effectiveness of the proposed method is demonstrated through a design example of a six‐pole SIW filter. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Taking advantage of collective knowledge in emergency response systems

When large groups work on a theme, they have the potential to produce a lot of useful knowledge, regardless of whether they are acting in a coordinated manner or individually. Spontaneously generated information has received much attention in recent years, as organizations and businesses discover the power of crowds. New technologies, such as blogs, Twitter, wikis, photo sharing, collaborative tagging and social networking sites, enable the creation and dissemination of content in a relatively simple way. As a result, the aggregate body of knowledge is growing at an accelerated rate. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Research has shown that it is possible to obtain high quality results from collectively produced work.In this paper, we consider the domain of emergency response. Research has shown that individuals respond quickly and massively to emergencies, and that they try to help with the situation. Thus, it seems like a logical step to attempt to harness collective knowledge for emergency management. Disaster relief groups and field command frequently suffer from lack of up to date information, which may be critical in a rapidly evolving situation. Some of this information could be generated by the crowd at large, enabling more effective response to the situation. In this paper, we discuss the possibilities for the introduction of collective knowledge in disaster relief and present architecture and examples of how this could be accomplished.