175 research outputs found
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
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
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
A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation
This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc
- …