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

Specific Activation of Estrogen Receptor Alpha and Beta Enhances Male Sexual Behavior and Neuroplasticity in Male Japanese Quail

Two subtypes of estrogen receptors (ER), ERα and ERβ, have been identified in humans and numerous vertebrates, including the Japanese quail. We investigated in this species the specific role(s) of each receptor in the activation of male sexual behavior and the underlying estrogen-dependent neural plasticity. Castrated male Japanese quail received empty (CX) or testosterone-filled (T) implants or were daily injected with the ER general agonist diethylstilbestrol (DES), the ERα-specific agonist PPT, the ERβ-specific agonist DPN or the vehicle, propylene glycol. Three days after receiving the first treatment, subjects were alternatively tested for appetitive (rhythmic cloacal sphincter movements, RCSM) and consummatory aspects (copulatory behavior) of male sexual behavior. 24 hours after the last behavioral testing, brains were collected and analyzed for aromatase expression and vasotocinergic innervation in the medial preoptic nucleus. The expression of RCSM was activated by T and to a lesser extent by DES and PPT but not by the ERβagonist DPN. In parallel, T fully restored the complete sequence of copulation, DES was partially active and the specific activation of ERα or ERβ only resulted in a very low frequency of mount attempts in few subjects. T increased the volume of the medial preoptic nucleus as measured by the dense cluster of aromatase-immunoreactive cells and the density of the vasotocinergic innervation within this nucleus. DES had only a weak action on vasotocinergic fibers and the two specific ER agonists did not affect these neural responses. Simultaneous activation of both receptors or treatments with higher doses may be required to fully activate sexual behavior and the associated neurochemical events

J Math Model Algor DOI 10.1007/s10852-007-9061-x Coevolutionary-based Mechanisms for Network Anomaly Detection

Abstract The paper presents an approach based on the principles of immune systems applied to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of the network behavior based on the self–nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. The structures corresponding to self-space are built using a training set from this space. The hyperrectangular detectors covering nonself space are created using a niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. The results of experiments show a high quality of intrusion detection, which outperform the quality of the recently proposed approach based on a hypersphere representation of the self-space. Key words artificial immune systems · anomaly detection problem · self–nonself space paradigm · hyperrectangular detectors · coevolutionary algorithms · computer networks

Hyper-heuristics for power-aware routing protocols

The idea underlying hyper-heuristics is to discover some combination of straightforward heuristics that performs very well across a whole range of problems. In this paper we describe genetic algorithm-based (GA) approach that learns such a heuristic combination for solving energy-efficient routing problem in mobile ad hoc networks (MANETs)

Function Optimization with Coevolutionary Algorithms

Abstract. The problem of parallel and distributed function optimization with coevolutionary algorithms is considered. Two coevolutionary algorithms are used for this purpose and compared with sequential genetic algorithm (GA). The first coevolutionary algorithm called a loosely coupled genetic algorithm (LCGA) represents a competitive coevolutionary approach to problem solving and is compared with another coevolutionary algoritm called cooperative coevolutionary genetic algorithm (CCGA). The algorithms are applied for parallel and distributed optimization of a number of test functions known in the area of evolutionary computation. We show that both coevolutionary algorithms outperform a sequential GA. While both LCGA and CCGA algorithms offer high quality solutions, they may compete to outperform each other in some specific test optimization problems.