FPF:UIN1009 Artificial Intelligence - Course Information
UIN1009 Artificial Intelligence
Faculty of Philosophy and Science in OpavaSummer 2014
- Extent and Intensity
- 2/0/0. 4 credit(s). Type of Completion: zk (examination).
- Teacher(s)
- doc. Ing. Petr Čermák, Ph.D. (lecturer)
prof. RNDr. Jozef Kelemen, DrSc. (lecturer) - Guaranteed by
- prof. RNDr. Jozef Kelemen, DrSc.
Institute of Computer Science – Faculty of Philosophy and Science in Opava - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Mathematics (programme MU, B1101)
- Applied Mathematics in Risk Management (programme MU, B1101)
- Computer Science and Technology (programme FPF, B1801 Inf)
- Mathematical Analysis (programme MU, M1101)
- Mathematical Methods in Economics (programme MU, B1101)
- Mathematics (programme MU, B1101)
- Computer Technology and its Applications (programme FPF, B1702 AplF)
- Secondary School Teacher Training in Computer Science (programme FPF, M7504)
- Course objectives
- Introduction, the history, and the Turing test. Reactivity vs. memory. Definition of the reactive agents, examples, case studies of the architecture. Decentralization and communication of agents, the subsumption architecture, artificial neural network, leasing and adaptability. The way from reactivity to representation on the example of the systems Toto and MetaToto. Specification of the notion knowledge, an example of the system STRIPS, and the deliberative robotics. The state space and search, qualitative and quantitative heuristics, the evaluation function and the system GPS. The associative representation and the natural language understanding problem. Procedural representation, calling procedures by goals, logic programming. The frame representation scheme, representation of the defaults, non-monoton inferences and logics. Learning systems, Summary.
- Syllabus
- 1. Introduction, history, and the Turing test.
2. Reactivity and deliberation, the subsumption architecture.
3. Decentralization and communication.
4. Artificial neural networks.
5. From subsumption to deliberation (from Toto to MetaToto).
6. Knowledge and STRIPS.
7. The state space and the search procedures, types of heuristics.
8. The General Problem Solver.
9. The associative representation scheme. Example of computational learning.
10. The procedural representation scheme, and calling programs by goals.
11. The frame representation scheme. Default values and non-monotonicity.
12. Resume.
- 1. Introduction, history, and the Turing test.
- Language of instruction
- Czech
- Further Comments
- The course can also be completed outside the examination period.
- Teacher's information
- * 75% attendance in course, active participation
* success rate of 70 % from written test, 30 % oral exam
- Enrolment Statistics (Summer 2014, recent)
- Permalink: https://is.slu.cz/course/fpf/summer2014/UIN1009