Case based polishing process planning with fuzzy set theory

It is difficult to make optimal process planning for polishing product because of the complex processes and the multi-criteria, attributes and vagueness of process parameters. To solve this problem, this paper combines the methodologies of Case Based Reasoning (CBR) and fuzzy Set Theory (FST) to support process planners in planning processes and making decisions effectively for polishing product. Moreover, various mathematical models are designed and integrated to the Web Based Portal System (WBPS) which supports the optimization computation of process parameter settings and case reasoning for polishing product. Finally, some cooker samples from the collaborating company have been collected to demonstrate the effectiveness of Case Based Process Planning (CBPP) model. © 2008 IEEE.published_or_final_versionThe IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2009), Singapore, 8-11 December 2008. In Proceedings of IEEM, 2008, p. 326-33

A fuzzy approach to similarity in Case-Based Reasoning suitable to SQL implementation

The aim of this paper is to formally introduce a notion of acceptance and similarity, based on fuzzy logic, among case features in a case retrieval system. This is pursued by rst reviewing the relationships between distance-based similarity (i.e. the standard approach in CBR) and fuzzy-based similarity, with particular attention to the formalization of a case retrieval process based on fuzzy query specication. In particular, we present an approach where local acceptance relative to a feature can be expressed through fuzzy distributions on its domain, abstracting the actual values to linguistic terms. Furthermore, global acceptance is completely grounded on fuzzy logic, by means of the usual combinations of local distributions through specic dened norms. We propose a retrieval architecture, based on the above notions and realized through a fuzzy extension of SQL, directly implemented on a standard relational DBMS. The advantage of this approach is that the whole power of an SQL engine can be fully exploited, with no need of implementing specic retrieval algorithms. The approach is illustrated by means of some examples from a recommender system called MyWine, aimed at recommending the suitable wine bottles to a customer providing her requirements in both crisp and fuzzy way

Contributions to artificial intelligence: the IIIA perspective

La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years

Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

International audienceA case-based reasoning system relies on different knowledge containers, including cases and adaptation knowledge. The knowledge acquisition that aims at enriching these containers for the purpose of improving the accuracy of the CBR inference may take place during design, maintenance, and also on-line, during the use of the system. This paper describes IakA, an approach to on-line acquisition of cases and adaptation knowledge based on interactions with an oracle (a kind of “ideal expert”). IakA exploits failures of the CBR inference: when such a failure occurs, the system interacts with the oracle to repair the knowledge base. IakA-NF is a prototype for testing IakA in the domain of numerical functions with an automatic oracle. Two experiments show how IakA opportunistic knowledge acquisition improves the accuracy of the CBR system inferences. The paper also discusses the possible links between IakA and other knowledge acquisition approaches