Flow matrices are widely used in many disciplines, but few methods can estimate them. This paper presents a knowledge-based system as capable of estimating and updating large-size time-evolving flow matrix. The system in this paper consists of two major components with the purposes of matrix estimation and parallel optimization. The matrix estimation algorithm interprets and follows users’ quer...

For more than 30 years, knowledge-based product configuration systems have been successfully applied in many industrial domains. Correspondingly, a large number of advanced techniques and algorithms have been developed in academia and industry to support different aspects of configuration reasoning. While traditional research in the field focused on the configuration of physical artefacts, reco...

Building a new GIS project is a major investment. Choosing the right GIS software package is critical to the success and failure of such investment. Because of the complexity of the problem a number of decision making tools must be deployed to arrive at the proper solution. In this study a new decision making approach for solving GIS software selection problem was proposed by integrating Expert...

Conceptual schemas have been recognized as an important tool for the design and evaluation of integrated databases and knowledge-based systems. The paper surveys the back end of a conceptual design methodology called ANNAPURNA. Its theoretical foundation that relies on multi-typed functional and existence depeudencies is discussed. Quality measures for conceptual schemes are introduced. A gener...

Configuration systems have widely been applied to efficiently address the customization responsiveness squeeze of companies dealing with Mass Cus-tomization. Over time, several frameworks have been introduced to enable their systematic planning , analyses, development and implementation. Traditional research has thereby either focused on defining modelling techniques for the configuration model...

Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiplyconnected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their co...

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretica...

Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs about a variable of interest in the network after the observation of some other variables. This is usually achieved under the assumption that the observations could reveal the actual states of the variables in a fully ...

Knowledge refinement tools seek to correct faulty knowledge based systems (KBSs) by identifying and repairing potentially faulty rules. The goal of the KRuSTWorks project is to provide a source of refinement components from which specialised refinement tools tailored to the needs of a range of KBSs are built. A core refinement algorithm reasons about the knowledge that has been applied, but thi...