—Nowadays, the application of data mining techniques in e-learning and web based adaptive educational systems is increasing exponentially. The discovered useful information can be used directly by the teacher or the author of the course to improve the instructional/learning performance. This can be an arduous task and therefore educational recommender systems are used in order to help the teach...

In this paper, we discuss in time domain the convergence of the iterative process for fractional-order systems. Fractional order iterative learning updating schemes are considered. The convergent conditions of the fractional-order and integer-order iterative learning schemes are proved to be equivalent. It is shown in Matlab/Simulink results that the tracking speed is the fastest when the syste...

In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach a stable result, i.e., a situation where no agent would wish to change its vote. One possibility is an iterat...

In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situation where no agent would wish to change its vote. ...

In this paper, the finite-time output consensus problem ofmulti-agent systems is considered by using the iterative learning control (ILC) approach. Two classes of distributed protocols are constructed from the two-dimensional system point of view (with time step and iteration number as independent variables), and are termed as iterative learning protocols. If learning gains are chosen appropria...

Prior work has introduced a form of explainable artificial intelligence that is able to precisely explain, in human-understandable form, why it makes decisions. It also learn make better decisions without potentially learning illegal or invalid considerations. This defensible system based on fractional value rule-fact expert systems and the use gradient descent training optimize rule weightings...

In this article we show how the accuracy of a rule based first order theory may be increased by combining it with a case-based approach in a classification task. Case-based learning is used when the rule language bias is exhausted. This is achieved in an iterative approach. In each iteration theories consisting of first order rules are induced and covered examples are removed. The process stops...

We investigate the principal learning capabilities of iterative learners in some more details. Thereby, we connne ourselves to study the learnability of indexable concept classes. The general scenario of iterative learning is as follows. An iterative learner successively takes as input one element of a text (an informant) for a target concept as well as its previously made hypothesis and output...

We investigate the principal learning capabilities of iterative learners in some more details. Thereby, we confine ourselves to study the learnability of indexable concept classes. The general scenario of iterative learning is as follows. An iterative learner successively takes as input one element of a text (an informant) for a target concept as well as its previously made hypothesis and outpu...