Modeling a domain in a tutorial-like system using learning automata

The aim of this paper is to present a novel approach to model a knowledge domain for teaching material in a Tutorial-like system. In this approach, the Tutorial-like system is capable of presenting teaching material within a Socratic model of teaching. The corresponding questions are of a multiple choice type, in which the complexity of the material increases in difficulty. This enables the Tutorial-like system to present the teaching material in different chapters, where each chapter represents a level of difficulty that is harder than the previous one. We attempt to achieve the entire learning process using the Learning Automata (LA) paradigm. In order for the Domain model to possess an increased difficulty for the teaching Environment, we propose to correspondingly reduce the range of the penalty probabilities of all actions by incorporating a scaling factor μ. We show that such a scaling renders it more difficult for the Student to infer the correct action within the LA paradigm. To the best of our knowledge, the concept of modeling teaching material with increasing difficulty using a LA paradigm is unique. The main results we have obtained are that increasing the difficulty of the teaching material can affect the learning of Normal and Below-Normal Students by resulting in an increased learning time, but it seems to have no effect on the learning behavior of Fast Students. The proposed representation has been tested for different benchmark Environments, and the results show that the difficulty of the Environments can be increased by decreasing the range of the penalty probabilities. For example, for some Environments, decreasing the range of the penalty probabilities by 50% results in increasing the difficulty of learning for Normal Students by more than 60%

Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students

Distributed learning automata-based scheme for classification using novel pursuit scheme

Author's accepted manuscript.Available from 03/03/2021.This is a post-peer-review, pre-copyedit version of an article published in Applied Intelligence. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10489-019-01627-w.acceptedVersio

On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments

The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. This fascinating paradox can be formulated in simple terms as trying to identify stochastic liars without any additional information about the truth. Though apparently impossible, we will show that it is feasible to solve the problem, a claim that is counterintuitive in and of itself. One aspect of our contribution is to show how redundancy can be introduced, and how it can be effectively utilized in resolving this paradox. Legacy work and the reported literature (for example, in the so-called weighted majority algorithm) have merely addressed assessing the reliability of a sensor by comparing its reading to the ground truth either in an online or an offline manner. Unfortunately, the fundamental assumption of revealing the ground truth cannot be always guaranteed (or even expected) in many real life scenarios. While some extensions of the Condorcet jury theorem [9] can lead to a probabilistic guarantee on the quality of the fused process, they do not provide a solution to the unreliable sensor identification problem. The essence of our approach involves studying the agreement of each sensor with the rest of the sensors, and not comparing the reading of the individual sensors with the ground truth—as advocated in the literature. Under some mild conditions on the reliability of the sensors, we can prove that we can, indeed, filter out the unreliable ones. Our approach leverages the power of the theory of learning automata (LA) so as to gradually learn the identity of the reliable and unreliable sensors. To achieve this, we resort to a team of LA, where a distinct automaton is associated with each sensor. The solution provided here has been subjected to rigorous experimental tests, and the results presented are, in our opinion, both novel and conclusive.Nivå

Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201