Aim of the course
The lecture is an introduction to the problems of artificial intelligence and knowledge engineering. The primary objective is to present to students the role played by information and knowledge in the modern world and familiarize them with the methods for the implementation of intelligent, knowledge based systems.
Characteristics of problem areas and basic concepts. Classification of AI systems. Methods of knowledge representation - two-and multi-valued logic, modal and temporal logic, semantic networks, decision trees and tables. Methods for inference in rule-based systems. Representation of uncertain and incomplete knowledge - fuzzy logic, rough sets. Information and decision-making systems based on the use of approximate and fuzzy logic. Problems of knowledge acquisition and exploration - the generation of association rules and decision trees. Basic problems of machine learning. Acquisition of knowledge through learning - neural networks, evolutionary algorithms. Neuro-fuzzy and fuzzy-neuro systems. Concept of intelligent agent. Reactive and cognitive agents. Agent systems as a decentralized information and decision systems. Problems of knowledge integration and management. Decision-making under uncertainty and conflict situations.
Overview of the course elements
Laboratory classes allow to practice skills in the use of artificial intelligence methods (in particular, rule-based systems, fuzzy logic, rough sets, neural networks and evolutionary algorithms) to construct modules of knowledge and decision systems, using available tools.
1. J.J. Mulawka: Expert systems
2. D.Rutkowska, M. Piliński, L. Rutkowska: Neural networks, genetic algorithms and fuzzy systems
3. L. Rutkowski: Methods and techniques of artificial intelligence
4. D. Goldberg: Genetic algorithms and their use
5. Z. Bubnicki: Introduction to expert systems