scholarly journals Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

2016 ◽  
Vol 11 (01) ◽  
pp. 38 ◽  
Author(s):  
Kennedy Efosa Ehimwenma ◽  
Martin Beer ◽  
Paul Crowther
Keyword(s):  
Learning Algorithm ◽  
Assessment System ◽  
Learning System ◽  
System Evaluation ◽  
Design Specification ◽  
Student Modelling ◽  
Preliminary Results ◽  
Estimation And Prediction ◽  
Computational Experimentation

Student modelling and agent classified rules learning as applied in the development of the intelligent Pre-assessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results it is gathered that the system has performed according to its design specification.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

scholarly journals Isotropic Sequence Order Learning

2003 ◽  
Vol 15 (4) ◽  
pp. 831-864 ◽  
Author(s):  
Bernd Porr ◽  
Florentin Wörgötter
Keyword(s):  
Weight Change ◽  
Closed Loop ◽  
Learning Algorithm ◽  
Linear Structure ◽  
Open Loop ◽  
Learning System ◽  
Behavioral Paradigm ◽  
Motor Loop ◽  
Loop Condition ◽  
Spike Time Dependent Plasticity

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, “ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm” (pp. 865–884).

scholarly journals Verification Of Independent Study Assessment Using Blockchain Technology

2020 ◽  
Vol 2 (11) ◽  
pp. 15-21
Author(s):  
Ria Dwi I’zzaty
Keyword(s):  
Teaching And Learning ◽  
Independent Study ◽  
Assessment System ◽  
Learning System ◽  
Assessment Process ◽  
Decentralized System ◽  
Blockchain Technology ◽  
Technological Developments ◽  
Verification Process ◽  
Student Grades

Current technological developments greatly impact the assessment verification system. To find out the student benchmarks in the results of teaching and learning activities during the learning system assessment process is very important in the scope of higher education. With the existence of blockchain technology widely applied in the world of Education, having the advantage of a decentralized system and strong cryptography can help universities in building infrastructure. Universitas Raharja is one of the educational institutions that has implemented an online assessment system (PEN +), which will use blockchain technology to verify the assessment of independent studies. which provides services to Raharja University lecturers in verifying student grades that can be accessed anywhere and at any time. However, currently the verification process for the independent study assessment that has been carried out is still done manually which results in verification not with very strong security. The existence of an independent study assessment verification uses blockchain technology to produce strong data security that did not occur before. In the PEN + lecturer assessment system for the independent study assessment verification process, it cannot yet enter the value of Independent study (IS) in real time. Therefore, there is a need for development in this blockchain technology for the verification process of independent study assessment. In this study there were 10 (ten) literary studies on verification of valuation. Thus there are several benefits that lecturers need not hesitate to verify the assessment, the process by using blockchain technology produces very strong security.

Valutare gli studenti per valutare il sistema: una esperienza della Provincia di Varese

2009 ◽  
pp. 123-140
Author(s):  
Emanuela Bonini ◽  
Alberto Vergani
Keyword(s):  
Learning Outcomes ◽  
Vocational Training ◽  
Assessment System ◽  
System Level ◽  
System Evaluation ◽  
Learning Outcomes Assessment ◽  
Crucial Component ◽  
Culture Models ◽  
Evaluation Of Learning ◽  
Italian Province

- The article presents the main results of a system level evaluation of learning outcomes concerning compulsory Vocational Training (VT) 3-years courses final formal examinations. This evaluation, carried out by the Education and Vocational Training Department of an italian Province (Varese), specifically deals with the use of single student learning standardized outcomes as a crucial component - if properly designed, managed and processed - of the evaluation of a local institutional VT system as a whole. This is quite a challenging task in Italy due both to the territorial differences existing in VT policies and institutional responsibilities and to the weakness of system evaluation culture, models and experiences in VT specifically and in education in general. The evaluation, conducted with the supervision of the two authors, will be presented and briefly discussed mainly by a methodological perspective. It will focus on the way in which learning contexts, training contents and trainees characteristics have been included into the evaluative analysis as factors potentially able: a) to explain, at a system level, the learning outcomes of the students; b) to offer the local stakeholders a comprehensive set of information and evidences to be used for the improvement of VT policies, programmes and single interventions.Key words: educational and vocational training, learning outcomes assessment, system evaluation, comparison, explicationParole chiave: istruzione e formazione professionale, valutazione di sistema, valutazione degli apprendimenti, comparazione, spiegazione

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Implementation of modified Q learning technique in EMCAP control architecture

2017 ◽  
Vol 7 (1.5) ◽  
pp. 269
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Cognitive Architecture ◽  
Dynamic Environment ◽  
Learning System ◽  
Selection Strategy ◽  
Q Learning ◽  
Learning Techniques ◽  
Markov Decision ◽  
Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture.  The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

The Development of Supervised Motion Learning and Vision System for Humanoid Robot

2019 ◽  
Vol 886 ◽  
pp. 188-193 ◽  
Author(s):  
Ssu Ting Lin ◽  
Jun Hu ◽  
Chia Hung Shih ◽  
Chiou Jye Huang ◽  
Ping Huan Kuo
Keyword(s):  
Humanoid Robot ◽  
Physical Mechanism ◽  
Learning Algorithm ◽  
Vision System ◽  
Humanoid Robots ◽  
Learning System ◽  
Automatic Learning ◽  
Swarm Optimization ◽  
Motion Learning ◽  
Important Research Topic

With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.

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