118,375 research outputs found
Development of a Refactoring Learning Environment
This paper describes a Refactoring Learning Environment, which is
intended to analyze and assess programming code, based on refactoring rules. The
Refactoring Learning Environment architecture includes an intelligent assistant –
Refactoring Agent, which is responsible for analysis and assessment of the code,
written by students in real time by using a set of refactoring methods. According to
the situation and based on the refactoring method, which should be applied, the
agent could react in different ways. Its goal is to show the student, as much as
possible, the weak places of his programming code and the possible ways to makes
it better
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
Designing intelligent computer‐based simulations: A pragmatic approach
This paper examines the design of intelligent multimedia simulations. A case study is presented which uses an approach based in part on intelligent tutoring system design to integrate formative assessment into the learning of clinical decision‐making skills for nursing students. The approach advocated uses a modular design with an integrated intelligent agent within a multimedia simulation. The application was created using an object‐orientated programming language for the multimedia interface (Delphi) and a logic‐based interpreted language (Prolog) to create an expert assessment system. Domain knowledge is also encoded in a Windows help file reducing some of the complexity of the expert system. This approach offers a method for simplifying the production of an intelligent simulation system. The problems developing intelligent tutoring systems are examined and an argument is made for a practical approach to developing intelligent multimedia simulation systems
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
Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design
Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design
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