714-0995/01 – Expert Systems in GIS (ESd)

Gurantor departmentDepartment of Mathematics and Descriptive GeometryCredits10
Subject guarantordoc. RNDr. František Staněk, Ph.D.Subject version guarantordoc. RNDr. František Staněk, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageCzech
Year of introduction2003/2004Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
STA22 doc. RNDr. František Staněk, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Combined Examination 20+0

Subject aims expressed by acquired skills and competences

The first part of this course is dedicated to solving expert problems. These problems can arise from other courses as well as from practice. The main emphasis lays in explanation of fundamental principles of problem solving strategies and of their general properties. The students learn how to decide which procedure is a suitable tool for solving a specific problem. An important ingredient of the course is algorithmic implementation in PROLOG language. The students learn how to use existing Expert Systems, too. The second part of the course deals with basic types of Artificial Neural Networks and with the way in which to understand these networks from both theoretical and practical points of view. The students are taught how to use these general methods to solve the problems arising from other courses of their study and from practice.

Teaching methods

Lectures
Individual consultations
Project work

Summary

Introduction to Artificial Intelligence (AI). Languages of AI. Introduction to PROLOG. Problem solving strategies. Expert System (ES). Describe the characteristics, features, structures, limitations, and benefits of ES. Describe the various methods of knowledge representation and build simple rule-based knowledge bases. Describe the various methods of inference. Conduct manual backward and forward chaining inferences. Artificial Neural Networks. The most common models like Backpropagation multilayered, Recurrent multilayered, Kohonen, Counterpropagation, Hopfield, BAM and ART nets are introduced. Object-oriented model of all mentioned types of neural networks. Expert System and Neural Network. Applications of Neural Network in Imaging Processing.

Compulsory literature:

Giarratano, J., Riley, G.: Expert Systems: Principles and Programming. 4th ed. Boston: Thomson Course Technology, 2005, 842 s.

Recommended literature:

Nilsson, N., J.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998.

Way of continuous check of knowledge in the course of semester

Individual consulting.

E-learning

Další požadavky na studenta

Active participation in consultations and successful defense of the project.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Introduction to Artificial Intelligence (AI). Languages of AI. Introduction to PROLOG. 2. Problem solving strategies. Expert System (ES). Describe the characteristics, features, structures, limitations, and benefits of ES. 3. Describe the various methods of knowledge representation and build simple rule-based knowledge bases. Describe the various methods of inference. Conduct manual backward and forward chaining inferences. 4. Artificial Neural Networks. 5. The most common models like Backpropagation multilayered. 6. Recurrent multilayered nets. 7. Kohonen, Counterpropagation nets are introduced. 8. Hopfield, BAM and ART nets are introduced. 9. Object-oriented model of all mentioned types of neural networks. 10. Expert System and Neural Network. Applications of Neural Network in Imaging Processing.

Conditions for subject completion

Combined form (validity from: 2013/2014 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2018/2019 (P3657) Geodesy, Cartography and Geoinformatics (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan
2018/2019 (P3646) Geodesy and Cartography (3602V002) Geoinformatics P Czech Ostrava Choice-compulsory study plan
2018/2019 (P3646) Geodesy and Cartography (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan
2017/2018 (P3657) Geodesy, Cartography and Geoinformatics (3602V002) Geoinformatics P Czech Ostrava Choice-compulsory study plan
2017/2018 (P3657) Geodesy, Cartography and Geoinformatics (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan
2017/2018 (P3646) Geodesy and Cartography (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan
2017/2018 (P3646) Geodesy and Cartography (3602V002) Geoinformatics P Czech Ostrava Choice-compulsory study plan
2016/2017 (P3646) Geodesy and Cartography (3602V002) Geoinformatics P Czech Ostrava Choice-compulsory study plan
2016/2017 (P3646) Geodesy and Cartography (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan
2015/2016 (P3646) Geodesy and Cartography (3602V002) Geoinformatics P Czech Ostrava Choice-compulsory study plan
2015/2016 (P3646) Geodesy and Cartography (3602V002) Geoinformatics K Czech Ostrava Choice-compulsory study plan

Occurrence in special blocks

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