178,305 research outputs found
Incorporating stakeholders’ knowledge in group decision-making
International audienc
A foundation for machine learning in design
This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD
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Integrating explanation-based and empirical learning methods in OCCAM
This paper discusses an approach to integrating empirical and explanation based learning techniques. The paper focuses on OCCAM, a program that has the capability to acquire via empirical means the knowledge needed for analytical learning. Two examples of this capability are discussed:The ability to use empirical techniques to acquire a domain theory for explanation based learning.The ability to use empirical learning techniques to find common patterns for causal relationships. These patterns encode a theory of causality (i.e., a set of general principles for recognizing causal relationships). Once acquired, a theory of causality can facilitate later learning by focusing on hypotheses which are consistent with the theory
Building an Expert System for Evaluation of Commercial Cloud Services
Commercial Cloud services have been increasingly supplied to customers in
industry. To facilitate customers' decision makings like cost-benefit analysis
or Cloud provider selection, evaluation of those Cloud services are becoming
more and more crucial. However, compared with evaluation of traditional
computing systems, more challenges will inevitably appear when evaluating
rapidly-changing and user-uncontrollable commercial Cloud services. This paper
proposes an expert system for Cloud evaluation that addresses emerging
evaluation challenges in the context of Cloud Computing. Based on the knowledge
and data accumulated by exploring the existing evaluation work, this expert
system has been conceptually validated to be able to give suggestions and
guidelines for implementing new evaluation experiments. As such, users can
conveniently obtain evaluation experiences by using this expert system, which
is essentially able to make existing efforts in Cloud services evaluation
reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and
Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24,
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A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition
Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a
compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its
practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed
Acquiring Correct Knowledge for Natural Language Generation
Natural language generation (NLG) systems are computer software systems that
produce texts in English and other human languages, often from non-linguistic
input data. NLG systems, like most AI systems, need substantial amounts of
knowledge. However, our experience in two NLG projects suggests that it is
difficult to acquire correct knowledge for NLG systems; indeed, every knowledge
acquisition (KA) technique we tried had significant problems. In general terms,
these problems were due to the complexity, novelty, and poorly understood
nature of the tasks our systems attempted, and were worsened by the fact that
people write so differently. This meant in particular that corpus-based KA
approaches suffered because it was impossible to assemble a sizable corpus of
high-quality consistent manually written texts in our domains; and structured
expert-oriented KA techniques suffered because experts disagreed and because we
could not get enough information about special and unusual cases to build
robust systems. We believe that such problems are likely to affect many other
NLG systems as well. In the long term, we hope that new KA techniques may
emerge to help NLG system builders. In the shorter term, we believe that
understanding how individual KA techniques can fail, and using a mixture of
different KA techniques with different strengths and weaknesses, can help
developers acquire NLG knowledge that is mostly correct
Computational aerodynamics and artificial intelligence
The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics
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