A group of intelligent agents may work together in order to solve a problem or achieve a common goal. If the group fails to achieve a goal, it may be able to adapt its behaviour so that such a goal can be achieved in the future. One of the ways in which the behaviour of the agent group can be changed is by reening the knowledge of individual agents. 1 Motivation Suppose a group of students are ...

The goal of our ARKTOS project is to build an intelligent knowledge-based system to classify satellite sea ice images. It involves acquiring knowledge from sea ice experts, quantifying such knowledge as computational entities, and ultimately building an intelligent classifier. In this paper we describe a two-stage knowledge engineering approach that facilitates explicit knowledge transfer, conv...

In the artificial intelligence field, knowledge representation and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge representation Methods has an important role in designing the systems. There have been many models for knowledge such as semantic networks, conceptual graphs, and neural networks. These models are useful tools t...

In artificial intelligence, there are many methods for knowledge representation. One of the effective models is the Computational Object Knowledge Base model (COKB model), which can be used to represent the total knowledge and to design the knowledge base component of practical intelligent systems. Besides, reasoning methods also play an important role in knowledge base systems. In fact, a popu...

Introduction We demonstrate an intelligent tutor that detects the content of collaborative behavior and offers relevant domain level interventions. Rashi is a domain independent intelligent tutor providing visitors with practice using inquiry skills. When working on human biology, visitors interact with a virtual sick patient whom they must successfully diagnose. Rashi supports visitors as they...

Intelligent agents are often faced with the problem of trying to merge possibly conflicting pieces of information obtained from different sources into a consistent view of the world. We propose a framework for the modelling of such merging operations with roots in the work of Spohn (Spohn 1988; Spohn 1991). Unlike most approaches we focus on the merging of epistemic states, not knowledge bases....

Our project is aimed at integrating and extending inferential, analogical, and intelligent IR technologies to create power tools for intelligence analysts. Our goal is to discover interesting and powerful functional integrations that permit these technologies to exploit each others’ strengths to mitigate their weaknesses. From the perspective of knowledge-based AI technology, a key goal of the ...

Next generation information system applications require powerful and intelligent information management that necessitates an efficient interaction between database and knowledge base technologies. It is also important for these applications to incorporate uncertainty in data objects, in integrity constraints, and/or in application. In this study, we propose an intelligent object-oriented databa...

It is recognized that the generic form of machine learning is a knowledge acquisition and manipulation process mimicking the brain. Therefore, knowledge representation as a dynamic concept network is centric in the design and implementation of the intelligent knowledge base of a Cognitive Learning Engine (CLE). This paper presents a Formal Knowledge Representation System (FKRS) for autonomous c...