Catalog Description :

Prerequisite:

Units:

Coordinator:

•  To differentiate the concepts of optimal reasoning and human-like reasoning, optimal behavior and human-like behavior. 
• To select an appropriate heuristic search algorithm for a problem and implement it by designing the necessary heuristic evaluation function. 
• To describe and use evolutionary algorithms.
• To describe and contrast the basic techniques for representing uncertainty.
• To explain the differences among the three main styles of learning: supervised, reinforcement, and unsupervised.
• To design and implement appropriate algorithms for a given problem

Current Texts:

• Michael NegnevitskyArtificial Intelligence, ISBN 0-201-71159-1

Topics:

• (IS1)Fundamental issues in intelligent systems History of artificial intelligence, the turing test, optimal vs. human-like reasoning, optimal vs. human-like behavior, the role of heuristics.
• (IS2) Search and constraint satisfaction A*, two-player games (minimax search, alpha-beta pruning).
• (IS3,IS5) Knowledge representation and reasoning knowledge representation and expert systems. Uncertainty, probabilistic reasoning, fuzzy sets and possibility theory, decision theory.
• (IS4) Advanced search Genetic algorithms, simulated annealing.
• (IS8) Machine learning and neural networks Definition and examples of machine learning, supervised learning, learning decision trees, learning neural networks, learning belief networks, the nearest neighbor algorithm, learning theory, the problem of overfitting, unsupervised learning, reinforcement learning.

ACM Sub Areas or Units Covered::

Laboratory:

Oral and Written Communication:

Social and Ethical Issues:

Problem Analysis:

Solution Design:

Version & Date

Version 1, 5/6/2003

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