Natural Learning Process Research Articles

EduChatbot: Implementing educational Chatbot for assisting the teaching-learning process by NLP-based hybrid heuristic adopted deep learning framework

Purpose A new Chatbot system is implemented to provide both voice-based and textual-based communication to address student queries without any delay. Initially, the input texts are gathered from the chat and then the gathered text is fed to pre-processing techniques like tokenization, stemming of words and removal of stop words. Then, the pre-processed data are given to the Natural Learning Process (NLP) for extracting the features, where the XLnet and Bidirectional Encoder Representations from Transformers (BERT) are utilized to extract the features. From these extracted features, the target-based fused feature pools are obtained. Then, the intent detection is carried out to extract the answers related to the user queries via Enhanced 1D-Convolutional Neural Networks with Long Short Term Memory (E1DCNN-LSTM) where the parameters are optimized using Position Averaging of Binary Emperor Penguin Optimizer with Colony Predation Algorithm (PA-BEPOCPA). Finally, the answers are extracted based on the intent of a particular student’s teaching materials like video, image or text. The implementation results are analyzed through different recently developed Chatbot detection models to validate the effectiveness of the newly developed model.Design/methodology/approach A smart model for the NLP is developed to help education-related institutions for an easy way of interaction between students and teachers with high prediction of accurate data for the given query. This research work aims to design a new educational Chatbot to assist the teaching-learning process with the NLP. The input data are gathered from the user through chats and given to the pre-processing stage, where tokenization, steaming of words and removal of stop words are used. The output data from the pre-processing stage is given to the feature extraction phase where XLnet and BERT are used. In this feature extraction, the optimal features are extracted using hybrid PA-BEPOCPA to maximize the correlation coefficient. The features from XLnet and features from BERT were given to target-based features fused pool to produce optimal features. Here, the best features are optimally selected using developed PA-BEPOCPA for maximizing the correlation among coefficients. The output of selected features is given to E1DCNN-LSTM for implementation of educational Chatbot with high accuracy and precision.Findings The investigation result shows that the implemented model achieves maximum accuracy of 57% more than Bidirectional long short-term memory (BiLSTM), 58% more than One Dimansional Convolutional Neural Network (1DCNN), 59% more than LSTM and 62% more than Ensemble for the given dataset.Originality/value The prediction accuracy was high in this proposed deep learning-based educational Chatbot system when compared with various baseline works.

Evaluating the AI dialogue System's intercultural, humorous, and empathetic dimensions in English language learning: A case study

The rise of artificial intelligence (AI) technologies has been notable in supporting English as an Additional Language (EAL) learners, particularly in offering interactive assessments. AI-driven dialogue systems that produce culturally relevant humor and empathy, bridging the native language (L1) and the target language (L2), have been employed to engage EAL learners more effectively and boost their learning results. However, there's a noticeable gap in research concerning the fusion of humor, empathy, and intercultural dimensions in language learning. Intercultural dimensions encompass acquiring crucial knowledge, communication skills, and the values essential for meaningful interactions across cultures. This case study examines the effectiveness of an AI dialogue system, termed the Multidimensional Approach Culture, Humor, and Empathy Bot (MACHE-Bot), in elevating the English language learning experience and its potential to enhance EAL proficiency. Using convenience sampling, 37 individuals were selected from an English language learning group on Facebook. Following a two-week pilot study, semi-structured interviews were conducted. The results revealed that MACHE-Bot adeptly initiated and reciprocated with humor, empathy, and cultural nuances in a manner well-received by participants. Additionally, the study identified five pivotal factors in EAL learning with MACHE-Bot: (1) intercultural dimensions, which include culturally humorous interaction and culturally empathetic support; (2) user trust comprising both affective and cognitive elements; (3) anthropomorphism; (4) reciprocal self-disclosure; and (5) higher engagement and motivation. These five factors collectively contribute to creating an engaging, relatable, and supportive learning environment, facilitating a more natural learning process, and fostering a deeper learner-bot connection, thereby potentially enhancing motivation and investment in the learning process.

Towards a Natural Learning Process?

Towards a Natural Learning Process?

A Thematic Analysis Exploration of Challenge-Based Learning Framework on Students of Academy X

Education development has grown to accommodate various teaching and learning styles in recent years. Challenge-Based Learning, or CBL, is a learning framework conceptualized in 2006 at Apple's Classroom of Tomorrow Today that is built from the natural learning process of human beings. It recognizes that everyone comes equipped with their skills, knowledge, and interests, which opens the possibility for collaboration and teamwork across various knowledge and skill backgrounds. Academy X is an institution that builds its curriculum and activity on the foundation of challenge-based learning. One of the purposes of the Academy is to increase the employability level of the students after they graduate. This study uses Thematic Analysis of interview results with Academy students to see how the CBL process in the Academy is encouraging students to grow and become more successful even after the Academy, especially in facing their careers. In summary, this study confirms through the interview of 10 Academy students how the Challenge-Based Learning drives the development of Growth Mindset in terms of Professional capacity and Personal Development despite the existence of Hindrances along the way. As such, CBL facilitates students’ growth and development at the Academy for their future careers.

Preserving and Nurturing Tausug Language: The Bahasa Sug Mobile Learning Application Tool for Enhancing Mother Tongue Development for Toddlers

In the fast-paced modern world, English and other commonly spoken languages have become prevalent even in Tausug households. This shift towards these languages presents difficulties in preserving and advancing the Tausug language. Therefore, it is essential to discover effective methods to cultivate language foundations and encourage the development of the mother tongue among Tausug toddlers. One hundred and sixty toddlers and 160 parents from eight Barangay in Jolo, Sulu, participated in a study. It was to investigate the effect between Bahasa Sug Mobile Learning Applications and traditional teaching methods in enhancing mother tongue development for toddlers. The results indicated high significance in favor of the Bahasa Sug Mobile Learning Application over the traditional teaching method, as shown by a t-test with a p-value of 0.05. The parents noted Bahasa Sug Mobile Apps' significant value in increasing their children's learning. Bahasa Sug, mobile learning applications, have become valuable tools for nurturing and preserving mother tongues in toddlers. These applications provide engaging and interactive experiences that align with children's natural learning processes. They can create an immersive and interactive environment for toddlers to develop and preserve their mother tongue. By embracing this mobile learning application, it can ensure the continuity and vitality of native languages, allowing children to maintain a strong connection with their cultural heritage and linguistic identity.

Skydiving technique analysis from a control engineering perspective: Developing a tool for studying human motor equivalence

This study offers a novel mathematical approach to sports technique analysis and motor equivalency investigation. The method is aimed to deal with intensive interaction between the environment and a trainee, and activities for which imitation learning is less efficient and movements must be established individually. The free-fall stage of skydiving is investigated, when aerial manoeuvres are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process of body flight is hard and protracted as the required movements are not similar to our natural movement repertoire, are often counter-intuitive, and highly dependant on anthropometric factors. The proposed method extracts the trainee’s movement patterns via Principal Component Analysis, and analyses the performed manoeuvres by modelling the dynamic response of the closed-loop feedback control system comprising the body actuated by each pattern and the environment. The method can provide a valuable insight into the subject’s learning process. The main novelty is that instead of comparing trainee’s movements to a template or a movement pattern, extracted from a top-rated athlete, we offer an independent way of technique analysis. Design tools of automatic control theory are utilised to suggest technique modifications that provide the desired performance improvement. An unconventional insight into the motor equivalence problem is achieved: Multiple body degrees-of-freedom are needed for shaping dynamic characteristics of the controlled plant. Movement patterns synergies provide a trade-off between these characteristics, e.g. stability and agility. The method is demonstrated on three case studies of aerial rotation of skilled, less-skilled, and elite skydivers.

Home Visit Learning Assistance as a Solution to Learning Problems During the Covid-19 Pandemic

This community service aims to obtain information regarding obstacles during the teaching and learning process in the network during the Covid-19 pandemic. This activity also aimed to reveal the effectiveness of study assistance by Community Service Teams of Universitas Muhammadiyah Sumatera Utara. The study in this activity uses the action research method to obtain information about the constraints and consequences of the Covid-19 pandemic for students and parents during the learning process with courage. The objects of this community service activity are school children who are supported in their natural learning process. Obstacles that occur in the subject of community service activities, namely mastery over the use of technology, which tends to be low due to the high cost of using or purchasing internet packages, besides that the subject has difficulty interacting directly with teachers and colleagues plus the assignments from the teacher are so many that it confuses students. The study results show that home visit learning can help students in their learning activities. Learning assistance is carried out openly by the research team. Community service activities were carried out in Desa Saentis, Percut Sei Tuan, Sumatra Utara

A Study on the Misconception of Art by Epistemological View

본 연구의 목적은 미술 학문의 개념 변화(지식 습득)를 인식론적 관점에서 탐색해 보는 것이었다. 연구의 내용을 정리하면 다음과 같다. 첫째, 미술교육자들이 미술이라는 학문의 정체성과 가치에 대한 깊은 이해를 가지고 있어야 한다. 그래야 학생들에게 미술적 개념에 대하여 설득력 있게 설명할 수 있다. 둘째, 미술교육자들이 오개념에 대한 인식론적 이해가 필요하다. 학습자들이 가지고 있는 인식론적 장애도 지식이다. 또한 학생들이 인식론적 전환을 가지는 것 자체가 쉽지 않은 일이다. 그럼에도 그러한 장애를 알고 극복하는 것이 학습의 핵심(Cornu, 1991)이라는 사실을 학생들이 인지하고 극복하도록 고무해야 한다. 셋째, 인식론적 관점을 전환시킬 수 있는 방법들을 습득하고 있어야 한다. 구체적인 방법으로 ‘인식론적 프레이밍’, ‘인지 갈등 상황’, ‘전형적 인식 상황’과 ‘결정적 예’ 등이 있다. 학문적 사고의 습득 과정에서 인식론적 장애가 발생하며, 이는 불가피한 것이고, 필수적인 것이다(Bachelard, 1970/1991). 새로운 학문적 사고는 기존의 모순적 사고를 버리고, 새로운 무모순적 사고를 습득하는 것이 아니라, 새로운 사고가 기존의 사고를 포용하여 확장되는 것이다.The purpose of this study was to investigate the misconception of art by epistemological view. The result of this study are summarized as follows. First, art educators should have profound understanding on the identity, value of the discipline of art. Then, art educators teach and demonstrate what is art. why artistic thinking is different with other principles, and how does it help students development. Second, art educators should have epistemological understanding on the misconceptions. The change of epistemological view is not easy, because the change of misconceptions require the change the attributes of epistemology. However, art educators should encourage students to recognize that to know and overcome the epistemological obstacles is the essence of learning(Cornu, 1991). Third, art educators should know the methods of change for epistemological view. They are ‘Epistemological Frame’, ‘Typically Perceived Situation’ and ‘Critical Example’, and ‘Cognitive Conflict Strategy’. The epistemological obstacles are inevitable(Bachelard, 1970/1991). They are part of the natural learning process. Art educators must try to understand that learning in education is not to abandon the old knowledge and accept new one but to embrace the new knowledge.

INTERNET DAN PERUBAHAN MEDIA SOSIAL: PEMANFAATAN MEDIA SOSIAL UNTUK LITERASI DAN ADVOKASI DI DRONE EMPRIT /MEDIA KERNELS

Currently, information and communication technology that is networked with the internet continues to grow rapidly, and the existence of the internet has provided openness to public information that seems as if there is no distance between individuals. The existence of technology has affected media consumption both directly and indirectly, so success in building digital literacy is an indication of modernization in the midst of public consumption. Changes in digital media consumers are also influenced by reader behavior; therefore, it is important to improve digital skills and literacy in the midst of technological and informational developments. One of the efforts is to support the use of Emprit drones with expertise in artificial intelligence (AI) and natural learning processes (NLP) through the Media Kernels software developed by Indonesians. Today, Indonesian people are entering the era of the Industrial Revolution 4.0 with the dissemination of information to the public in a modern digital society where online media or mass media play an important role in social media because they are interconnected and interact with each other among media users. The research method used is qualitative, with a descriptive approach, conducted through interviews with two (expert-recommended) sources. The results of the study show that the strategic role of social media in this digitalization era is influenced by the formation of public opinion, and today's technology has also taken root in people's lives. So, with the help of the internet, spatial limitations can still exist, including in the 3T areas (left behind, frontier, outermost).

An automatic control system with human-in-the-loop for training skydiving maneuvers: Proof-of-concept experiment

A real-time motion training system for skydiving is proposed. Aerial maneuvers are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process is extremely slow due to unfamiliar free-fall dynamics, stress induced blocking of kinesthetic feedback, and complexity of the required movements. The key idea is to augment the learner with an automatic control system that would be able to perform the trained activity if it had direct access to the learner’s body as an actuator. The aiding system will supply the following visual cues to the learner: 1. Feedback of the current body posture; 2. The body posture that would bring the body to perform the desired maneuver; 3. Prediction of the future inertial position and orientation if the body retained its present posture. This paper presents results of a Proof-of-Concept experiment, whereby humans controlled a virtual skydiver free-falling in a computer simulation, by the means of their bodies. This task was impossible without the aiding system, enabling all participants to complete the task at the first attempt. The design of stabilizing closed loop control of highly non-linear and unstable dynamics with a human in the loop proved to be feasible. The future goal is applying this novel concept to aid novices maintain stability in free-fall and perceive the unfamiliar environmental dynamics, thus accelerating the initial stages of skill acquisition.

DEVELOPMENT OF GEOSPATIAL INFORMATION INTEGRATED WITH BIG DATA TO AGRICULTURAL HAZARD MONITORING IN WEST JAVA

Abstract. Food security is highly dependent on three aspects, namely food availability, food access, and food utilization. The availability aspect depends on food supply which is identical to agricultural productivity. West Java Province is the third national rice producer with 16.6%, but West Java Province is the most extensive rice consumer, around 21.1% of the total national rice consumption. Agricultural productivity can decline due to natural hazards such as floods and droughts. Monitoring floods and droughts in paddy fields are necessary to prevent decreased agricultural productivity. This study aims to monitor the rice fields from the dangers of flooding and drought every month. Agricultural hazard monitoring is divided into two parameters, namely static parameters and dynamic parameters. Dynamic parameters are observed every month so that the hazard index is generated on a monthly scale. GIS and Remote sensing data are integrated to perform agricultural hazard modelling. Furthermore, this agricultural hazard modelling results will be strengthened by using big to provide information about an almost real-time event that can be accessed through the Application Program Interface (API) service. This study uses a data mining system from Drone Emprit that performs data mining on Twitter and news portals with machine learning technology (probabilistic classifier) and Natural Learning Process. The results obtained are around 15,000 data from January 1 to November 1, 2021, and 37.9% of them are identified by location based on the city or district level in West Java Province. It is hoped that the policy-maker can consider the area of agricultural land that requires assistance to increase productivity and plan a policy to support agriculture in West Java in the future.

MODEL PEMBELAJARAN SETS (SCIENCE, ENVIRONMENTAL, TECHNOLOGY, SOCIETY) BERBASIS BUDAYA LOKAL PADA PEMBELAJARAN IPA TERHADAP BERFIKIR KRITIS DAN PEDULI LINGKUNGAN

This study describes the contribution of SETS (Science, Environment, Technology, Society) model based on local culture on the improvement of creative thinking ability and environmental care of junior high school students in natural science instructional. The study was done by literature approaches by using relevant research results. By implementation of SETS model syntax with local culture enrichment, the research results showed there was the improvement of critical thinking of the junior high school student and also its environmental care in natural science learning process. Therefore, the natural science teachers were asked to implement the SETS model in natural science instruction in order to reach the learning of natural science objectives, especially in critical thinking and environmental care.

Улс төрийн дискурс судалгаанд тм аргыг ашиглах нь буюу орчин үеийн чиг хандлага

Text mining as an approach, referred to a text data mining, is the text analytics process of normalizing, integrating and grouping big data information including free (unstructured) texts and speeches unto the well-organized schematic models according to the research made by Cutting, 1992. Thus it is developed more in exploring text analysis, armed with Artificial intelligence, Natural learning process and other computational statistics applied in natural language as the mathematical approaches, Hearst (1999). Currently TM is used as the statistical tool approaches based on the key words which make mental model of the texts in discourse. One example is IBM’s Watson (Upbin, 2013). Further, with the support of TM algorithm, it is highly advantage of deriving important databases by its value from both texts and speeches with the methods such as sentiment analysis, clustering, figuring texts into images, macro structure and mental modelling in discourse of linguistic. Therefore, text mining is interdisciplinary manner approaches for data analysis, information processing, categorization in combination of mathematical statistics and linguistic analysis. To be collecting statistic databases, analyzing topic models, creating language model under the text mining approach, it brings us the specific characteristics of linguistics as text mining is to allow tounderstand the texts in different levels such as sentence, proposition, paragraph, context, idea of the whole discourse and to pre-calculate relations in between ideas and key and chosen words of the speakers. Text mining is also used for allocating, sub-parting and grouping the data into structured models of key concepts. Specifically, in linguistics, the text mining is importantly advised to release the same thought data the same as human language understands and processes. Another significance of the text mining, it works very well when variety different types of documents and data in various types including verbal and nonverbal through the language use are observed and created in semantic network which is fundamental upon to the topic concepts. The semantic network and nodes in key words is worthwhile to calculate correlations in between words’ meanings and values of the key expressions in the context of the discourse. With this advantage, this paper is explored the opportunities on how to parallel research on the political discourse and its global concepts and macro structures in the way the speakers choose the appropriate language and how they express their ideas, ideologies, political positions, implications, speech trends, and emotions in words-one hand, it is analyzed by the researcher’s view and other hand, NLP, ML, IA algorithms and mathematical statistics are used for collecting research data and mapping the superstructure and imaging global concepts and discourses-are in need of macro level analysis of discourse. And all the results from comprehensive method and TM approaches are re-proved according to the reliability of the analysis through the quantitative and qualitative analysis.

Partnership between patients and interprofessional healthcare providers along the multifaceted journey to medication adherence.

Partnership between patients and interprofessional healthcare providers along the multifaceted journey to medication adherence.

The Role of the Endogenous Opioid System in the Vocal Behavior of Songbirds and Its Possible Role in Vocal Learning.

The opioid system in the brain is responsible for processing affective states such as pain, pleasure, and reward. It consists of three main receptors, mu- (μ-ORs), delta- (δ-ORs), and kappa- (κ-ORs), and their ligands – the endogenous opioid peptides. Despite their involvement in the reward pathway, and a signaling mechanism operating in synergy with the dopaminergic system, fewer reports focus on the role of these receptors in higher cognitive processes. Whereas research on opioids is predominated by studies on their addictive properties and role in pain pathways, recent studies suggest that these receptors may be involved in learning. Rodents deficient in δ-ORs were poor at recognizing the location of novel objects in their surroundings. Furthermore, in chicken, learning to avoid beads coated with a bitter chemical from those without the coating was modulated by δ-ORs. Similarly, μ-ORs facilitate long term potentiation in hippocampal CA3 neurons in mammals, thereby having a positive impact on spatial learning. Whereas these studies have explored the role of opioid receptors on learning using reward/punishment-based paradigms, the role of these receptors in natural learning processes, such as vocal learning, are yet unexplored. In this review, we explore studies that have established the expression pattern of these receptors in different brain regions of birds, with an emphasis on songbirds which are model systems for vocal learning. We also review the role of opioid receptors in modulating the cognitive processes associated with vocalizations in birds. Finally, we discuss the role of these receptors in regulating the motivation to vocalize, and a possible role in modulating vocal learning.

The Development of "MAS AL" (Alternative Assessment Management) as a Solution to Integrated Distance Assessment Management

In line with the outbreak of the Covid-19 virus in all corners of the world, various learning ideas that can be done during the pandemic have emerged. The natural learning process is changed to Learn From Home, Online learning, Distance Learning, Hybrid Learning, and others. Submission of material even to the collection of assignments is also made online. Many teachers end up choosing methods that are considered easy and mastered. Some teachers use Google Drive, email, YouTube, WhatsApp, and others. However, teachers still find it challenging to organize the files because each platform has limitations. Departing from the teacher's need to manage learning assessments, researchers are interested in developing an application that can manage online and integrated learning assessment data. The application is named MAS AL which is an acronym for Alternative Assessment Management. This study aims to 1) describe how the process of developing the "MAS AL" application and 2) describe the feasibility of the MAS AL application. This study uses the ADDIE development model. The ADDIE stage consists of 5 stages: Analyze, Design, Develop, Implement, and Evaluate. The validation test result obtained a percentage of 89.2% for the first validator and 90.3% for the second validator. At the implementation stage, MAS AL trial was carried out by elementary school teachers in East Java who obtained a questionnaire percentage of 97.5%. So, the conclusion obtained is that MAS AL as a solution to integrated distance assessment management is appropriate.

A stable proportion of Purkinje cell inputs from parallel fibers are silent during cerebellar maturation

Cerebellar Purkinje neurons integrate information transmitted at excitatory synapses formed by granule cells. Although these synapses are considered essential sites for learning, most of them appear not to transmit any detectable electrical information and have been defined as silent. It has been proposed that silent synapses are required to maximize information storage capacity and ensure its reliability, and hence to optimize cerebellar operation. Such optimization is expected to occur once the cerebellar circuitry is in place, during its maturation and the natural and steady improvement of animal agility. We therefore investigated whether the proportion of silent synapses varies over this period, from the third to the sixth postnatal week in mice. Selective expression of a calcium indicator in granule cells enabled quantitative mapping of presynaptic activity, while postsynaptic responses were recorded by patch clamp in acute slices. Through this approach and the assessment of two anatomical features (the distance that separates adjacent planar Purkinje dendritic trees and the synapse density), we determined the average excitatory postsynaptic potential per synapse. Its value was four to eight times smaller than responses from paired recorded detectable connections, consistent with over 70% of synapses being silent. These figures remained remarkably stable across maturation stages. According to the proposed role for silent synapses, our results suggest that information storage capacity and reliability are optimized early during cerebellar maturation. Alternatively, silent synapses may have roles other than adjusting the information storage capacity and reliability.

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics

The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. It’s essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. The first part of the overview introduces Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. In part two, we continue to introduce applications in transportation, industries, communications and networking, etc. and discuss the limitations of DRL.

Ji Zimanê Dayîkê Wêdetir: Kurdî Wekî Zimanekî Herêmî û Xwecih

The association between language and territory is particularly significant in terms of indigenousness. Therefore, the argument of this paper is to examine this aspect of the Kurdish language through the interpretation of the Kurdish political movement in Turkey based on self-identification, an essential component of indigeneity. Since the 1990s, the Kurdish political movement has been demanding mother-tongue rights with the growing grassroots support. This study examines pro-Kurdish political party programs, statements/press releases, and interviews with deputies of the HDP, current parliamentary party. The article explores acquiring Kurdish as a native language by Kurds and non-Kurds in the region, mainly through mothers, homes, and the community environment. The process of acquiring the Kurdish language varies according to how individuals interact with the local Kurdish community, such as marrying into Kurdish families, systematic settlement, and growing up in the region. Whilst Kurdish is regarded as the mother language of the Kurds by Kurds and non-Kurds members of the Kurdish political movement in Turkey, this article draws further attention to the regional and local aspects of Kurdish, including its natural learning process of non-Kurds within the region.

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing

The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. Although efficient algorithms are being widely used, it seems essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. A follow-up paper will cover applications in transportation, communications and networking, and industries.