Using metarules to integrate knowledge in knowledge based systems. An application in the woodworking industry

The current study addresses the integration of knowledge obtained from Data Mining structures and models into existing Knowledge Based solutions. It presents a technique adapted from commonKADS and spiral methodology to develop an initial knowledge solution using a traditional approach for requirement analysis, knowledge acquisition, and implementation. After an initial prototype is created and verified, the solution is enhanced incorporating new knowledge obtained from Online Analytical Processing, specifically from Data Mining models and structures using meta rules. Every meta rule is also verified prior to being included in the selection and translation of rules into the Expert System notation. Once an initial iteration was completed, responses from test cases were compared using an agreement index and kappa index. The problem domain was restricted to remake and rework operations in a cabinet making company. For Data Mining models, 8,674 cases of Price of Non Conformance (PONC) were used for a period of time of 3 months. Initial results indicated that the technique presented sufficient formalism to be used in the development of new systems, using Trillium scale. The use of 50 additional cases randomly selected from different departments indicated that responses from the original system and the solution that incorporated new knowledge from Data Mining differed significantly. Further inspection of responses indicated that the new solution with additional 68 rules was able to answer, although with an incorrect alternative in 28 additional cases that the initial solution was not able to provide a conclusion

A Conceptual Framework for Agent Definition and Development

The use of agents of many different kinds in a variety of fields of computer science and artificial intelligence is increasing rapidly and is due, in part, to their wide applicability. The richness of the agent metaphor that leads to many different uses of the term is, however, both a strength and a weakness: its strength lies in the fact that it can be applied in very many different ways in many situations for different purposes; the weakness is that the term agent is now used so frequently that there is no commonly accepted notion of what it is that constitutes an agent. This paper addresses this issue by applying formal methods to provide a defining framework for agent systems. The Z specification language is used to provide an accessible and unified formal account of agent systems, allowing us to escape from the terminological chaos that surrounds agents. In particular, the framework precisely and unambiguously provides meanings for common concepts and terms, enables alternative models of particular classes of system to be described within it, and provides a foundation for subsequent development of increasingly more refined concepts