Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Emotion and Adaptation

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

The theory of affordances

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

The aim of all education is to apply what we learn in different contexts and to recognise and extend this learning to new situations. Virtual learning environments can be used to build skills. Recent research in cognitive psychology and education has shown that acquisitions are linked to the initial context. This provides a challenge for virtual reality in education or training. A brief overview of transfer issues highlights five main ideas: (1) the type of transfer enables the virtual environment (VE) to be classified according to what is learned; (2) the transfer process can create conditions within the VE to facilitate transfer of learning; (3) specific features of VR must match and comply with transfer of learning; (4) transfer can be used to assess a VE’s effectiveness; and (5) future research on transfer of learning must examine the singular context of learning. This paper discusses how new perspectives in cognitive psychology influence and promote transfer of learning through the use of VEs.

Transfer of learning in virtual environments: a new challenge?

This study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we explain how we integrated in MASCARET models necessary to the creation of Intelligent Tutoring System. We notably incorporate pedagogical strategies and pedagogical actions. We present pedagogical agents. To validate our model, the SECUREVI application for fire-fighters training is developed.

https://hal.archives-ouvertes.fr/hal-00610857/document

MASCARET: pedagogical multi-agents systems for virtual environment for training

Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. However, plant diseases are threatening the livelihood of this important source. Convolutional neural networks (CNN) have demonstrated great performance (beating that of humans) in object recognition and image classification problems. This paper describes the feasibility of CNN for plant disease classification for leaf images taken under the natural environment. The model is designed based on the LeNet architecture to perform the soybean plant disease classification. 12,673 samples containing leaf images of four classes, including the healthy leaf images, were obtained from the PlantVillage database. The images were taken under uncontrolled environment. The implemented model achieves 99.32% classification accuracy which show clearly that CNN can extract important features and classify plant diseases from images taken in the natural environment.

Soybean Plant Disease Identification Using Convolutional Neural Network

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Mascaret: Pedagogical multi-agents system for virtual environment for training. Cédric Buche, Ronan Querrec, Pierre De Loor, Pierre Chevaillier

MASCARET: A Pedagogical Multi-Agent System for Virtual Environments for Training

The context of this research is the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase) into a virtual environment. The aim of this environment is to instruct the learner, and to assist the instructor. The proposed system is created using a multi-agent system. This system emits a set of knowledge (actions carried out by the learner, knowledge about the field, etc.) which Pegase uses to make informed decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance.

https://www.enib.fr/~buche/article/IJVR_10.pdf

Cédric Buche

Papers

Transfer of learning in virtual environments: a new challenge?

MASCARET: pedagogical multi-agents systems for virtual environment for training

Soybean Plant Disease Identification Using Convolutional Neural Network

MASCARET: A Pedagogical Multi-Agent System for Virtual Environments for Training

PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments