Second Generation Expert Systems: A Step Forward in Knowledge Engineering

David, Jean-Marc; Krivine, Jean-Paul; Simmons, Reid

doi:10.1007/978-3-642-77927-5_1

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…This study is a remarkable example of developing a hybrid artificial intelligence system to solve real-world based problems. The hybrid system introduced here has the potential to be part of some modular components to derive feedback for people/users (according to meaning of future states of time series) and to form general medical artificial intelligence-based systems like expert systems and/or medical/clinical decision support systems [126][127][128][129][130]. Furthermore, it may be also a small part of larger, adaptive control systems which continuously support real-time processes performed in medical/healthcare locations.…”

Section: Discussionmentioning

confidence: 99%

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse

2018

Applied Sciences

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The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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Section: Discussionmentioning

confidence: 99%

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse

2018

Applied Sciences

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“…With complex domains, the obvious solution of simply adding new rules for each previously unencountered pattern is not possible, as the rule base to cover all possible cases would be large and unwieldy (Hirsch et al, 1989;Weintraub et al, 1990). In addition, increasing the size of the rule base leads to an increased potential for negative interactions between rules and the possibility of rules cancelling each other out, thus rendering the system unable to solve problems that it had previously solved with a smaller rule base (David et al, 1993a). As a consequence of this, systems operating in complex domains often use simplifying assumptions to reduce the size of the rule base (Weintraub et al, 1990).…”

Section: Motivations and Limitationsmentioning

confidence: 95%

The use of artificial intelligence in the analysis of sports performance: A review of applications in human gait analysis and future directions for sports biomechanics

Lapham

Bartlett²

1995

Journal of Sports Sciences

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Computers have played an important supporting role in the development of experimental and theoretical sports biomechanics. The role of the computer now extends from data capture and data processing through to mathematical and statistical modelling and simulation and optimization. This paper seeks to demonstrate that elevation of the role of the computer to involvement in the decision-making process, through the use of artificial intelligence techniques, would be a potentially rewarding future direction for the discipline. In the absence of significant previous work in this area, this paper reviews experiences in a parallel field of medical informatics, namely gait analysis. Research into the application of expert systems and neural networks to gait analysis is reviewed, observations made and comparisons drawn with the biomechanical analysis of sports performance. Brief explanations of the artificial intelligence techniques discussed in the paper are provided. The paper concludes that the creation of an expert system for a specific well-defined sports technique would represent a significant advance in the development of sports biomechanics.

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“…For example, Santillan-Gutierrez and Wright (1996) applied genetic algorithms to derive promising solutions during the development of a product [23]. Rao et al (1999) reported that the various areas include but are not limited to problem solving and planning [24], expert systems [25], knowledge-based systems [26], natural language processing [27], robotics [28], computer vision [29], learning [30], genetic algorithms [31], neural networks [32], case-based reasoning [33], rough set theory [34], and intelligent agent [35]. A mixture of various areas of artificial intelligence was also utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design

Lee

2021

Sustainability

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Emerging technologies such as artificial intelligence help operations management achieve sustainability. However, in sustainable operations management studies, scholars pay less attention to product design, which can be highly affected by artificial intelligence. In addition, sustainability is perceived as maintaining economic development while limiting environmental harm caused by human activity. Therefore, social sustainability is treated as peripheral compared to economic and environmental sustainability. However, social sustainability now has gained more attention because it is the basis on which meaningful economic and environmental sustainability can be valid. Thus, I systematically reviewed present studies on product design considering artificial intelligence to understand what types of social sustainability are achieved when applying artificial intelligence to product design. This study discovered artificial intelligence can improve social sustainability in product design, but social sustainability diversity is necessary. These findings can contribute to the inclusion of different types of social sustainability in product design when using artificial intelligence.

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Second Generation Expert Systems: A Step Forward in Knowledge Engineering

Cited by 15 publications

References 23 publications

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

The use of artificial intelligence in the analysis of sports performance: A review of applications in human gait analysis and future directions for sports biomechanics

A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design

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