Design an Intelligent System to automatically Tutor the Method for Solving Problems

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Hien D. Nguyen ◽  
◽  
Dung A. Tran ◽  
Huan P. Do ◽  
Vuong T. Pham ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Public Administration ◽  
Real World ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Problem Solver ◽  
High School Mathematics ◽  
Knowledge Model ◽  
Real World Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems.

Fuzzy and Probabilistic Object-Oriented Databases

2011 ◽  
pp. 1606-1611
Author(s):  
Tru H. Cao
Keyword(s):  
Probability Theory ◽  
Fuzzy Logic ◽  
Hybrid System ◽  
Real World ◽  
Interdisciplinary Research ◽  
Intelligent Systems ◽  
Research Effort ◽  
Object Oriented ◽  
Object Oriented Databases ◽  
Real World Problems

For modeling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other’s weakness. In particular, object orientation provides a hierarchical data abstraction scheme and a mechanism for information hiding and inheritance. However, the classical object-oriented data model cannot deal with uncertainty and imprecision pervasive in real world problems. Meanwhile, probability theory and fuzzy logic provide measures and rules for representing and reasoning with uncertainty and imprecision. That has led to intensive research and development of fuzzy and probabilistic object-oriented databases, as collectively reported in De Caluwe (1997), Ma (2005), and Marín & Vila (2007).

Unexplainability and Incomprehensibility of AI

2020 ◽  
Vol 07 (02) ◽  
pp. 277-291 ◽  
Author(s):  
Roman V. Yampolskiy
Keyword(s):  
Real World ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Impossibility Results

Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly, it is important to understand how an intelligent system functions for safety and security reasons. In this paper, we describe two complementary impossibility results (Unexplainability and Incomprehensibility), essentially showing that advanced AIs would not be able to accurately explain some of their decisions and for the decisions they could explain people would not understand some of those explanations.

Fuzzy and Probabilistic Object-Oriented Databases

2009 ◽  
pp. 126-134
Author(s):  
Tru H. Cao
Keyword(s):  
Hybrid System ◽  
Real World ◽  
Interdisciplinary Research ◽  
Intelligent Systems ◽  
Research Effort ◽  
Systems Integration ◽  
Object Oriented ◽  
Object Oriented Databases ◽  
Real World Problems

For modeling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other’s weakness.

LEARNING STEPPINGSTONES FOR PROBLEM SOLVING

1993 ◽  
Vol 07 (03) ◽  
pp. 527-540
Author(s):  
DAVID RUBY ◽  
DENNIS KIBLER
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Real World ◽  
Logic Synthesis ◽  
Vlsi Design ◽  
Problem Solver ◽  
Problem Difficulty ◽  
Learning Problem ◽  
Real World Problems

One goal of Artificial Intelligence is to develop and understand computational mechanisms for solving difficult real-world problems. Unfortunately, domains traditionally used in general problem-solving research lack important characteristics of real-world domains, making it difficult to apply the techniques developed. Most classic AI domains require satisfying a set of Boolean constraints. Real-world problems require finding a solution that meets a set of Boolean constraints and performs well on a set of real-valued constraints. In addition, most classic domains are static while domains from the real world change. In this paper we demonstrate that SteppingStone, a general learning problem solver, is capable of solving problems with these characteristics. SteppingStone heuristically decomposes a problem into simpler subproblems, and then learns to deal with the interactions that arise between the subproblems. In lieu of an agreed upon metric for problem difficulty, we choose significant problems that are difficult for both people and programs as good candidates for evaluating progress. Consequently we adopt the domain of logic synthesis from VLSI design to demonstrate SteppingStone’s capabilities.

scholarly journals Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hien D. Nguyen ◽  
Nhon V. Do ◽  
Nha P. Tran ◽  
Xuan Hau Pham ◽  
Vuong T. Pham
Keyword(s):  
Knowledge Representation ◽  
Stem Education ◽  
Intelligent Systems ◽  
Vital Role ◽  
Specification Language ◽  
Problem Solver ◽  
Knowledge Model ◽  
Science Technology ◽  
Representation Method ◽  
Mathematics And Physics

Nowadays, building intelligent systems for science, technology, engineering, and math (STEM) education is necessary to support the studying of learners. Intelligent problem solver (IPS) is a system that can be able to solve or tutor how to solve the problems automatically. Learners only declare hypothesis and goal of problems based on a sufficient specification language. They can request the program to solve it automatically or to give instructions that help them to solve it themselves. Knowledge representation plays a vital role in these kinds of intelligent systems. There are various methods for knowledge representation; however, they do not meet the requirements of an IPS in STEM education. In this paper, we propose the criteria of a knowledge model for an IPS in education. These criteria orient to develop a method for knowledge representation to meet actual requirements in practice, especially pedagogical requirements. For proving the effectiveness of these criteria, a knowledge model is also constructed. This model can satisfy these criteria and be applied to build IPS for courses, such as mathematics and physics.

Knowledge-Based Model of Expert Systems Using Rela-Model

2018 ◽  
Vol 28 (08) ◽  
pp. 1047-1090 ◽  
Author(s):  
Nhon V. Do ◽  
Hien D. Nguyen ◽  
Ali Selamat
Keyword(s):  
High School ◽  
Formal Ontology ◽  
Problem Solver ◽  
Mathematical Foundation ◽  
High School Mathematics ◽  
Knowledge Model ◽  
Knowledge Domains ◽  
Knowledge Based ◽  
Solid Geometry ◽  
Theoretical Foundations

Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundations, these methods cannot guarantee criteria in knowledge representation such as formality, universality, usability and practicality. They are not adequate to represent the knowledge domains in practice which have many components. Based on formal ontology approach, a knowledge model about relations, called Rela-model, is presented in this paper. It has the components such as concepts, relations between concepts, and rules. The concepts in this model consist of attributes, facts and rules of itself. Each object in a concept has also equipped its behavior to solve problems on it. The methods for solving problems based on Rela-model are also studied. The general problems on this model are the following: Given some objects and facts on them, determine the closure of set of attributes and facts on the objects or determine an object or consider a relation between the objects. The algorithms to solve problems are designed and their properties, such as finiteness, effectiveness, have also been proved. Besides the solid mathematical foundation, Rela-model also has a simple specification language which can effectively represent the knowledge, thus it can be used in many real situations. Our approach is also applied to build two systems: the intelligent problem solver about solid geometry in high school mathematics, and the expert system to diagnose diseases in diabetic microvascular complication.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

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