Because of the complexity and fuzziness of the real world, it’s hard to build a dense knowledge system and reason in it with traditional methods. But man can deal with such tasks freely. Inspired by cognition and linguistics, a solution is advanced for reasoning dense knowledge in this paper. Objects and concepts are organized in the form of concept graph. Soaking the nodes in the graph until t...

The current focus of his work is the design of cognitively informed learning and knowledge management systems that are more able to support the learner and the knowledge worker. In particular, Thierry Nabeth is the coordinator of the AtGentive IST STREP project, aiming at the design of personalised agentbased systems supporting the management of Attention. Thierry Nabeth is also interested in t...

The AI research group in the College of Computing currently consists of six full-time academic faculty members: Ronald C. Arkin, Kurt P. Eiselt, Ashok K. Goel, Janet L. Kolodner, Daryl T. Lawton, and Ashwin Ram. It also includes Richard Billington, a full-time research scientist, and about 20 graduate students. As a group, we cover a fairly large portion of the AI research spectrum, including t...

Class-based languages express knowledge in terms of objects and classes, and have inspired a huge number of formalisms in computer science. Description logics form a family of both class-based and logic-based knowledge representation languages which allow for modeling an application domain in terms of objects, classes and relationships between classes, and for reasoning about them. This paper p...

Knowledge modelling, a main activity for the development of Knowledge Based Systems, have no set standards and are mostly done in an ad hoc way. There is a lack of support for the transition from abstract level to implementation. In this paper, a methodology for the development of the knowledge model, which is inspired by both Software and Knowledge Engineering, is proposed. Use of UML which is...

Knowledge acquisition and modeling are important steps in developing knowledge-based systems and especially multi-agent systems. This paper presents a set of different models for knowledge acquisition and modeling for multi-agent systems. Our model set extends the KADS methodology to include distributed and cooperative aspects. Model templates are composed of generalized knowledge sources and g...

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in...

Inspired by the Google Wonder Wheel, in this paper we present Semantic Wonder Cloud (SWOC): a tool that helps users in knowledge exploration within the DBpedia dataset by adopting a hybrid approach. We describe both the architecture and the user interface. The system exploits not only pure semantic connections in the underlying RDF graph but it mixes the meaning of such information with externa...