Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

FML-based Prediction Agent and Its Application to Game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.Comment: 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 201

Strategies for online personalised nutrition advice employed in the development of the eNutri web app

The internet has considerable potential to improve health-related food choice at low-cost. Online solutions in this field can be deployed quickly and at very low cost, especially if they are not dependent on bespoke devices or offline processes such as the provision and analysis of biological samples. One key challenge is the automated delivery of personalised dietary advice in a replicable, scalable and inexpensive way, using valid nutrition assessment methods and effective recommendations. We have developed a web-based personalised nutrition system (eNutri) which assesses dietary intake using a validated graphical FFQ and provides personalised food-based dietary advice automatically. Its effectiveness was evaluated during an online randomised controlled trial dietary intervention (EatWellUK study) in which personalised dietary advice was compared with general population recommendations (control) delivered online. The present paper presents a review of literature relevant to this work, and describes the strategies used during the development of the eNutri app. Its design and source code have been made publicly available under a permissive open source license, so that other researchers and organisations can benefit from this work. In a context where personalised diet advice has great potential for health promotion and disease prevention at-scale and yet is not currently being offered in the most popular mobile apps, the strategies and approaches described in the present paper can help to inform and advance the design and development of technologies for personalised nutrition

Формирование персонализированного рациона питания с использованием структурной оптимизации

The design of a human personalized diet considering a variety of different factors is associated with system analysis and formalization of data and knowledge, as well as with the development of digital technologies. The paper presents the methodology of optimization and formation of personalized diets based on structural-parametric modeling. The proposed approach allows solving the following tasks: 1)  to analyze the daily diet or individual meals (breakfast, lunch, afternoon snack, dinner, additional meals or snacks) with a known quantitative set of finished products in terms of energy value and chemical composition in order to reveal dietary disorders; 2)  to calculate quantity of products optimal for a meal from the fixed list, thereby composing an individual reference diet with regard to the mental and physical activities, nutritive status of a consumer and economic aspects; 3) to optimize a diet depending on the task at hand by selecting a group of finished products from a complete or selected list of archival data, equally taking into account all the necessary parameters; 4) to adjust the diet taking into account dietary deviations in certain parameters of the chemical composition and energy value by additional introduction of special purpose products with the increased biological value, multivitamin and multivitamin-mineral supplements, as well as natural bioactive substances.Конструирование персонализированного рациона питания человека с учетом многообразия различных факторов связано с системным анализом и формализацией накопленных данных и знаний, а также с развитием цифровых технологий. В работе представлена методология оптимизации и формирования персонализированных рационов питания на основе структурно-параметрического моделирования. Предлагаемый подход позволяет решать следующие задачи: 1) анализировать суточный рацион или отдельные приемы пищи (завтрак, обед, полдник, ужин, дополнительные приемы пищи (перекус)) с известным количественным набором готовых продуктов по энергетической ценности и химическому составу с целью выявления диетических нарушений; 2) рассчитывать оптимальное для приема пищи количество продуктов из фиксированного перечня, тем самым составляя индивидуальный эталонный рацион с учетом умственной и физической нагрузки, нутритивного статуса потребителя, а также экономических аспектов; 3) оптимизировать рацион в зависимости от поставленной задачи путем подбора группы готовых продуктов из полного или избранного перечня архивных данных, равнозначно учитывая при этом все необходимые параметры; 4) корректировать рацион питания с учетом диетических отклонений по отдельным параметрам химического состава и энергетической ценности за счет дополнительного введения продуктов повышенной биологической ценности специального назначения, поливитаминных и поливитаминно-минеральных препаратов, а также природных биологически активных веществ

JFML: A Java Library to Design Fuzzy Logic Systems According to the IEEE Std 1855-2016

Fuzzy logic systems are useful for solving problems in many application fields. However, these systems are usually stored in specific formats and researchers need to rewrite them to use in new problems. Recently, the IEEE Computational Intelligence Society has sponsored the publication of the IEEE Standard 1855-2016 to provide a unified and well-defined representation of fuzzy systems for problems of classification, regression, and control. The main aim of this standard is to facilitate the exchange of fuzzy systems across different programming systems in order to avoid the need to rewrite available pieces of code or to develop new software tools to replicate functionalities that are already provided by other software. In order to make the standard operative and useful for the research community, this paper presents JFML, an open source Java library that offers a complete implementation of the new IEEE standard and capability to import/export fuzzy systems in accordance with other standards and software. Moreover, the new library has associated a Website with complementary material, documentation, and examples in order to facilitate its use. In this paper, we present three case studies that illustrate the potential of JFML and the advantages of exchanging fuzzy systems among available softwareThis work was supported in part by the XXII Own Research Program (2017) of the University of Córdoba, in part by the Spanish Ministry of Economy and Competitiveness under Grants RYC-2016-19802 (Ramón y Cajal contract), TIN2017-84796-C2-1-R, TIN2014-56633-C3-3-R, TIN2014-57251-P, and TIN2015-68454-R, in part by the Andalusian Government under Grant P11-TIC-7765, in part by the Xunta de Galicia (accreditation 2016-2019), and in part by the European Union (European Regional Development Fund)