Methods of artificial intelligence
Description: The discipline is devoted to the study of issues related to the use of artificial intelligence methods in the design of modern automation systems for technical objects. The main stages and directions of development of artificial intelligence, features of building knowledge-based systems, basic strategies for finding solutions to artificial intelligence problems, as well as models for representing and using knowledge are considered: production, frame approaches, semantic networks, formal logical models, methods for processing fuzzy knowledge . The main methods of artificial intelligence used in the analysis, development and implementation of intelligent systems are described.
Amount of credits: 6
Пререквизиты:
- Automation of Engineering Systems
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 30 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 90 |
| Form of final control | Exam |
| Final assessment method | oral exam |
Component: University component
Cycle: Profiling disciplines
Goal
- Formation of theoretical and practical skills for solving research and applied problems related to the design of complex controlled systems based on the use of artificial intelligence methods to solve problems of managing technical systems operating under conditions of uncertainty.
Objective
- studying the main approaches underlying artificial intelligence methods and methods for their implementation;
- mastering methods of representation and methods of working with knowledge in the development of intelligent control systems for technical objects;
- developing skills in preparing data, converting them into knowledge and creating knowledge representation models for machine processing;
- developing skills in using mathematical decision-making apparatus based on expert systems, fuzzy logic, artificial neural networks and genetic algorithms for building control systems for technical objects.
Learning outcome: knowledge and understanding
- describe basic models and means of knowledge representation, methods for finding solutions in various types of state spaces
Learning outcome: applying knowledge and understanding
- apply knowledge representation tools when constructing a model of a given subject area;
- select artificial intelligence methods for solving practical problems and evaluate the feasibility of using them in specific tasks;
Learning outcome: formation of judgments
- explain the natural scientific essence of problems arising in the course of professional activities in the field of modeling and analysis of complex natural and artificial systems;
Learning outcome: communicative abilities
- demonstrate a culture of scientific research, including using modern information and communication technologies
Learning outcome: learning skills or learning abilities
- apply knowledge of basic methods of artificial intelligence in subsequent professional activities as researchers, university teachers, engineers;
Teaching methods
modular learning technology
information and communication technologies
technologies of educational and research activities
Assessment of the student's knowledge
Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.
| Period | Type of task | Total |
|---|---|---|
| 1 rating | Laboratory work "Artificial Intelligence Methods" | 0-100 |
| Laboratory work "Architecture of an artificial intelligence system" | ||
| Laboratory work "Representation of knowledge in the form of productions" | ||
| Laboratory work "Semantic networks" | ||
| Laboratory work "Frame model of knowledge representation" | ||
| Laboratory work "Formal logical models" | ||
| Laboratory work "Representation of declarative knowledge about the concept of the studied subject area" | ||
| Boundary control 1 | ||
| 2 rating | Laboratory work "Fuzzy models" | 0-100 |
| Laboratory work "Design of an expert system" | ||
| Laboratory work "Methods of expert evaluation" | ||
| Laboratory work "Expert system based on Bayesian belief networks" | ||
| Laboratory work "Neural network models" | ||
| Laboratory work "Evolutionary computation" | ||
| Boundary control 2 | ||
| Total control | Exam | 0-100 |
The evaluating policy of learning outcomes by work type
| Type of task | 90-100 | 70-89 | 50-69 | 0-49 |
|---|---|---|---|---|
| Excellent | Good | Satisfactory | Unsatisfactory | |
| Completion and defense of laboratory assignments | Completed the work in full in compliance with the required sequence of actions; in the answer, correctly and accurately completes all records, tables, pictures, drawings, graphs, calculations; performs error analysis correctly. When answering questions, he correctly understands the essence of the question, gives an accurate definition and interpretation of basic concepts; accompanies the answer with new examples, knows how to apply knowledge in a new situation; can establish a connection between the material being studied and previously studied, as well as with the material acquired in the study of other disciplines. | Completed the work as required for an “excellent” rating, but there were 2-3 shortcomings. The student’s answer to the questions satisfies the basic requirements, but is given without applying knowledge in a new situation, without using connections with previously studied material and material learned in the study of other disciplines; If one mistake or no more than two shortcomings are made, the student can correct them independently or with a little help from the teacher. | Completed the work not completely, but not less than 50% of the volume, which allows you to obtain the correct results and conclusions; Errors were made during the work. When answering questions, the student correctly understands the essence of the question, but in the answer there are some problems in mastering the course questions that do not interfere with further mastery of the program material; no more than one gross error and two omissions were made. | Completed the work in full in compliance with the required sequence of actions; in the answer, correctly and accurately completes all records, tables, pictures, drawings, graphs, calculations; performs error analysis correctly. When answering questions, he correctly understands the essence of the question, gives an accurate definition and interpretation of basic concepts; accompanies the answer with new examples, knows how to apply knowledge in a new situation; can establish a connection between the material being studied and previously studied, as well as with the material acquired in the study of other disciplines. |
| Oral interview | The answer qualitatively reveals the content of the topic. The answer is well structured. The conceptual apparatus has been perfectly mastered. Demonstrated a high level of understanding of the material. Excellent ability to formulate thoughts and discuss controversial issues provisions. | The main issues of the topic are revealed. The structure of the answer is generally adequate to the topic. Well mastered conceptual apparatus. Demonstrated a good level of understanding of the material. Good ability to formulate thoughts and discuss controversial issues. | The topic is partially covered. The answer is poorly structured. The conceptual apparatus has been partially mastered. Understanding of individual provisions from the material on the topic. Satisfactory ability to formulate ideas thoughts, discuss controversial points. | The answer qualitatively reveals the content of the topic. The answer is well structured. The conceptual apparatus has been perfectly mastered. Demonstrated a high level of understanding of the material. Excellent ability to formulate thoughts and discuss controversial issues provisions. |
Evaluation form
The student's final grade in the course is calculated on a 100 point grading scale, it includes:
- 40% of the examination result;
- 60% of current control result.
The final grade is calculated by the formula:
| FG = 0,6 | MT1+MT2 | +0,4E |
| 2 |
Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;
E is a digital equivalent of the exam grade.
Final alphabetical grade and its equivalent in points:
The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:
| Alphabetical grade | Numerical value | Points (%) | Traditional grade |
|---|---|---|---|
| A | 4.0 | 95-100 | Excellent |
| A- | 3.67 | 90-94 | |
| B+ | 3.33 | 85-89 | Good |
| B | 3.0 | 80-84 | |
| B- | 2.67 | 75-79 | |
| C+ | 2.33 | 70-74 | |
| C | 2.0 | 65-69 | Satisfactory |
| C- | 1.67 | 60-64 | |
| D+ | 1.33 | 55-59 | |
| D | 1.0 | 50-54 | |
| FX | 0.5 | 25-49 | Unsatisfactory |
| F | 0 | 0-24 |
Topics of lectures
- Introduction to artificial intelligence
- Knowledge representation in intelligent systems
- Search in state space
- Production model for knowledge representation
- Representation of knowledge by semantic networks
- Frame model of knowledge representation
- Logical model for knowledge representation
- Representation of fuzzy knowledge
- Fundamentals of fuzzy logic
- Expert systems
- Technology for developing expert systems
- Eliciting knowledge from experts
- Processing of expert assessments
- Expert systems with uncertain knowledge
- Bayesian belief networks as a tool for developing expert systems
- Artificial neural networks
- Multilayer neural networks
- Methods of evolutionary modeling
Key reading
- Modeli i metody iskusstvennogo intellekta : ucheb. posobie / T. G. Pen'kova, Yu. V. Vajnshtejn. – Krasnoyarsk : Sib. feder. un-t, 2019. – 116 s.
- Smolin D.V. Vvedenie v iskusstvennyj intellekt: konspekt lekcij. - M.: FIZMATLIT, 2017. - 208s.
- Ostrouh A.V. Vvedenie v iskusstvennyj intellekt: monografiya – Krasnoyarsk: Nauchno-innovacionnyj centr, 2020. – 250 s.
- Rodzin S.I., Rodzina O.N. Modeli predstavleniya znanij. praktikum po kursu \"Sistemy iskusstvennogo intellekta\": uchebnoe posobie. -Taganrog: Izd-vo YuFU, 2014. - 151 s.
- Habarov S. Ekspertnye sistemy. Konspekt lekcij [Elektronnyj resurs]. - ttp://www.habarov.spb.ru/main_es.htm
- Rostovcev V.S. Iskusstvennye nejronnye seti : uchebnik dlya vuzov / V. S. Rostovcev. - 2-e izd., ster. - Sankt-Peterburg : Lan', 2021. - 216 s.
- Rutkovskaya D., Pilin'skij M., Rutkovskij L. Nejronnye seti, geneticheskie algoritmy i nechetkie sistemy: per. s pol'sk. - M.: Goryachaya liniya - Telekom, 2006. - 452s.
Further reading
- Borovskaya E.V. , Davydova N.A. Osnovy iskusstvennogo intellekta: uchebnoe posobie. -M.: Laboratoriya znanij, 2020. -130 s.
- Dzharratano D., G. Rajli. Ekspertnye sistemy. Principy razrabotki i programmirovanie. - M.: Izd. Vil'yams, 2011. - 775 s.
- Dzhekson Piter. Vvedenie v ekspertnye sistemy : [Per. s angl. V. T. Tertyshnogo] / Piter Dzhekson. - 3. izd. - M. [i dr.] : Vil'yams, 2001. - 622 s.
- Таулли Т. Основы искусственного интеллекта: нетехнические введение: пер. с англ. – СПб.: БХВ-Петербург, 2021.–288с.
- Sirichenko A. V. Intellektual'nye sistemy kontrolya i upravleniya. Praktikum : uchebnoe posobie / A. V. Sirichenko. — Moskva : MISIS, 2020. — 24 s.
- Sirichenko A. V. Iskusstvennye nejronnye seti. Praktikum : uchebnoe posobie / A. V. Sirichenko. — Moskva : MISIS, 2022. — 26 s.
