REASONING IN INTELLIGENT SYSTEMS
The course is for 3rd year undergraduates and
graduate students of CS. We will mostly study the problem of reasoning in
presence of uncertainty. Different approaches to this problem are considered:
Certainty Factor calculation, Probabilistic approach, Dempster-Shafer
theory of evidence, Fuzzy Logic etc. In this connection different aspects
of Learning Theory are discussed. In the last part the new development
connected with these problems: Knowledge Discovery in Databases is considered.
- Historical background:
Artificial Intelligence, Problems of Reasoning and
- Precise Reasoning. Logic and Theorem Proving.
Rules of Inference, Resolution Proofs.
- Reasoning in Rule-Based, Frame-Based and Semantic Net-Based Systems.
- Problems with application of logic based approach to Common Sence
Reasoning. Reasoning with Uncertainty. Certainty Factor.
- Probabilistic Reasoning, Pooling of Evidences. Recursive Bayessian
- Bayessian Probabilistic Network (BPN). Representation of BPN in the
form of DAG. Connection between structure of DAG and properties of BPN.
- Abductive Reasoning and Explaining Away in BPN.
- Learning probabilities in BPN.
- BPN and Markov Random Fiel (MRF). Comparison and Applications.
- Dempster-Shafer Theory of Evidence.
- Probabilistic Reasoning and Decision Making. Risk Function and Utility
Function based approaches.
- Fuzzy Logic and Fuzzy Logic based controllers. Fuzzy Logic and
- Inference in Fuzzy Logic based systems.
- Learning in Intelligent Systems. Inductive Reasoning (IR).
- IR with Tree-like structures. Cluster Analysis and IR.
- IR and Artificial Neural Nets.
Knowledge Discovery in Databases. Requirements and Methods.
- Review of some optimization methods (Sumulating Annealing, Genetic
Algorithms, Evolutionary Programming) and their application to IR.
Assignements: 6 homeworks with exercises. At the end of
the course the students should pass exams in the form of a special homework
(during three days). The final mark is mostly based on the result of this
final homework. The results of mentioned 6 homeworks also have an influence
on the final mark.
Requirements: Standard courses on Mathematics (Probability
and Statistics, Linear Algebra, Infi).
P.H. Winston, Artificial Intelligence, Addison-Wesley, 1984.
J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann,
A.Kandel, Fuzzy Expert Systems, CRC Press, 1991.
Editors: U.M. Fayyad et others, Advances in Knowledge Discovery and Data
Mining, AAAI Press/The MIT Press, 1996.