Download BBE 4102 Intelligent Systems

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Computer vision wikipedia , lookup

Wizard of Oz experiment wikipedia , lookup

Philosophy of artificial intelligence wikipedia , lookup

Existential risk from artificial general intelligence wikipedia , lookup

Collaborative information seeking wikipedia , lookup

Personal knowledge base wikipedia , lookup

Embodied cognitive science wikipedia , lookup

Genetic algorithm wikipedia , lookup

Incomplete Nature wikipedia , lookup

Ethics of artificial intelligence wikipedia , lookup

AI winter wikipedia , lookup

Knowledge representation and reasoning wikipedia , lookup

History of artificial intelligence wikipedia , lookup

Transcript
BBE 4102 Intelligent Systems
Course description
Theory and implementation of a variety of techniques used to simulate intelligent behavior. Expert
systems, fuzzy logic, neural networks, evolutionary computation, and two-player game-tree search will
be covered in depth. Knowledge representation, pattern recognition, hybrid approaches, and handling
uncertainty will also be discussed
Course Objectives
By covering the course in Intelligent Systems, the student will be able to:
1. Appreciate the concepts of Artificial Intelligence and the diversity of approaches and definitions
with which it is associated.
2. Develop an understanding of heuristic methods.
3. Learn the underlying theory and practice of evolutionary computation, including genetic
algorithms and genetic programming.
4. Appreciate knowledge engineering, develop expert systems, and understand fuzzy expert
systems.
5. Develop an understanding of and implement artificial neural networks.
6. Implement a two-player strategy game with optimized adversarial search.
7. Implement, observe and evaluate alternative approaches to intelligent systems
Course Content
Optimization Methods
 Gradient methods
 Linear Programming
 Constrained Problems and Lagrange Multiplier Method
 Search Method
 Ordinal Optimization
 Genetic Algorithms
 Applications
Fundamentals of Neural Networks
 Basic concepts
 Back-propagation algorithm
 Applications
Advanced Neural Networks
 Competitive learning
 Data clustering networks
 Application in hierarchical modeling for complex systems
Knowledge Representation Methods
 Linguistic knowledge representation
 Mathematical foundation: Random Sets
 Applications
Information Fusion Techniques
 Fusion of linguistic and stochastic information
 Application in intelligent segmentation
 Application in sensor fusion
Reference Materials
1. Michael Negnevitsky, 2005. Artificial Intelligence: A Guide to Intelligent Systems. AddisonWesley- ISBN 0321204662
72
2. George F. Luger, Peder Johnson, Jean E. Newman, Carl Stern, Ronald Yeo - Cognitive Science:
The Science of Intelligent Systems. Academic Press (1994) - ISBN 0124595707
3. Jatinder N. D. Gupta, Guisseppi A. Forgionne, Manuel Mora. Intelligent Decision-making
Support Systems: Foundations, Applications and Challenges. Springer (2006)-ISBN 1846282284
Requirements
Hours per Semester
LH
45
PH
30
TH
00
CH
60
Weighted
Total Mark
Weighted
Exam Mark
Weighted Continuous
Assessment Mark
Credit
Units
WTM
100
WEM
60
WCM
40
CU
4